309 pubblicazioni classificate nel seguente modo:

Glucagon-like peptide-1 and interleukin-6 interaction in response to physical exercise: An in-silico model in the framework of immunometabolism
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
Autore/i: Morettini, M.; Palumbo, M. C.; Bottiglione, A.; Danieli, A.; Del Giudice, S.; Burattini, L.; Tura, A.
Classificazione: 1 Contributo su Rivista
Abstract: Background and objective: Glucagon-like peptide 1 (GLP-1) is classically identified as an incretin hormone, secreted in response to nutrient ingestion and able to enhance glucose-stimulated insulin secretion. However, other stimuli, such as physical exercise, may enhance GLP-1 plasma levels, and this exercise-induced GLP-1 secretion is mediated by interleukin-6 (IL-6), a cytokine secreted by contracting skeletal muscle. The aim of the study is to propose a mathematical model of IL-6-induced GLP-1 secretion and kinetics in response to physical exercise of moderate intensity. Methods: The model includes the GLP-1 subsystem (with two pools: gut and plasma) and the IL-6 subsystem (again with two pools: skeletal muscle and plasma); it provides a parameter of possible clinical relevance representing the sensitivity of GLP-1 to IL-6 (k0). The model was validated on mean IL-6 and GLP-1 data derived from the scientific literature and on a total of 100 virtual subjects. Results: Model validation provided mean residuals between 0.0051 and 0.5493 pg⋅mL-1 for IL-6 (in view of concentration values ranging from 0.8405 to 3.9718 pg⋅mL-1) and between 0.0133 and 4.1540 pmol⋅L-1 for GLP-1 (in view of concentration values ranging from 0.9387 to 17.9714 pmol⋅L-1); a positive significant linear correlation (r = 0.85, p<0.001) was found between k0 and the ratio between areas under GLP-1 and IL-6 curve, over the virtual subjects. Conclusions: The model accurately captures IL-6-induced GLP-1 kinetics in response to physical exercise.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/326556

A Minimal and Multi-Source Recording Setup for Ankle Joint Kinematics Estimation During Walking Using Only Proximal Information From Lower Limb
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
Autore/i: Mobarak, R.; Tigrini, A.; Verdini, F.; Al-Timemy, A. H.; Fioretti, S.; Burattini, L.; Mengarelli, A.
Classificazione: 1 Contributo su Rivista
Abstract: In this study, a minimal setup for the ankle joint kinematics estimation is proposed relying only on proximal information of the lower-limb, i.e. thigh muscles activity and joint kinematics. To this purpose, myoelectric activity of Rectus Femoris (RF), Biceps Femoris (BF), and Vastus Medialis (VM) were recorded by surface electromyography (sEMG) from six healthy subjects during unconstrained walking task. For each subject, the angular kinematics of hip and ankle joints were synchronously recorded with sEMG signal for a total of 288 gait cycles. Two feature sets were extracted from sEMG signals, i.e. time domain (TD) and wavelet (WT) and compared to have a compromise between the reliability and computational capacity, they were used for feeding three regression models, i.e. Artificial Neural Networks, Random Forest, and Least Squares - Support Vector Machine (LS-SVM). BF together with LS-SVM provided the best ankle angle estimation in both TD and WT domains (RMSE < 5.6 deg). The inclusion of Hip joint trajectory significantly enhanced the regression performances of the model (RMSE < 4.5 deg). Results showed the feasibility of estimating the ankle trajectory using only proximal and limited information from the lower limb which would maximize a potential transfemoral amputee user's comfortability while facing the challenge of having a small amount of information thus requiring robust data-driven models. These findings represent a significant step towards the development of a minimal setup useful for the control design of ankle active prosthetics and rehabilitative solutions.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/327112

Software Packages and Tools for the Analysis of Continuous Glucose Monitoring Data
DIABETES TECHNOLOGY & THERAPEUTICS
Autore/i: Piersanti, Agnese; Giurato, Francesco; Göbl, Christian; Burattini, Laura; Tura, Andrea; Morettini, Micaela
Classificazione: 1 Contributo su Rivista
Abstract: The advancement of technology in the field of glycemic control has led to the widespread use of continuous glucose monitoring (CGM), which can be nowadays obtained from wearable devices equipped with a minimally invasive sensor, that is, transcutaneous needle type or implantable, and a transmitter that sends information to a receiver or smart device for data storage and display. This work aims to review the currently available software packages and tools for the analysis of CGM data. Based on the purposes of this work, 12 software packages have been identified from the literature, published until December 2021, namely: GlyCulator, EasyGV (Easy Glycemic Variability), CGM-GUIDE(C) (Continuous Glucose Monitoring Graphical User Interface for Diabetes Evaluation), GVAP (Glycemic Variability Analyzer Program), Tidepool, CGManalyzer, cgmanalysis, GLU, CGMStatsAnalyser, iglu, rGV, and cgmquantify. Comparison of available software packages and tools has been done in terms of main characteristics (i.e., publication year, presence of a graphical user interface, availability, open-source code, number of citations, programming language, supported devices, supported data format and organization of the data structure, documentation, presence of a toy example, video tutorial, data upload and download, measurement-units conversion), preprocessing procedures, data display options, and computed metrics; also, each of the computed metrics has been analyzed in terms of its adherence to the American Diabetes Association (ADA) 2017 international consensus on CGM data analysis and the ADA 2019 international consensus on time in range. Eventually, the agreement between metrics computed by different software and tools has been investigated. Based on such comparison, usability and complexity of data management, as well as the possibility to perform customized or patients-group analyses, have been discussed by highlighting limitations and strengths, also in relation to possible different user categories (i.e., patients, clinicians, researchers). The information provided could be useful to researchers interested in working in the diabetic research field as to clinicians and endocrinologists who need tools capable of handling CGM data effectively.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/308564

Automatic diagnosis of newly emerged heart failure from serial electrocardiography by repeated structuring & learning procedure
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
Autore/i: Sbrollini, A.; Barocci, M.; Mancinelli, M.; Paris, M.; Raffaelli, S.; Marcantoni, I.; Morettini, M.; Swenne, C. A.; Burattini, L.
Classificazione: 1 Contributo su Rivista
Abstract: Heart failure (HF) diagnosis, typically visually performed by serial electrocardiography, may be supported by machine-learning approaches. Repeated structuring & learning procedure (RS&LP) is a constructive algorithm able to automatically create artificial neural networks (ANN); it relies on three parameters, namely maximal number of hidden layers (MNL), initializations (MNI) and confirmations (MNC), arbitrarily set by the user. The aim of this study is to evaluate RS&LP robustness to varying values of parameters and to identify an optimized combination of parameter values for HF diagnosis. To this aim, the Leiden University Medical Center HF database was used. The database is constituted by 129 serial ECG pairs acquired in patients who experienced myocardial infarction; 48 patients developed HF at follow-up (cases), while 81 remained clinically stable (controls). Overall, 15 ANNs were created by considering 13 serial ECG features as inputs (extracted from each serial ECG pair), 2 classes as outputs (cases/controls), and varying values of MNL (1, 2, 3, 4 and 10), MNI (50, 250, 500, 1000 and 1500) and MNC (2, 5, 10, 20 and 50). The area under the curve (AUC) of the receiver operating characteristic did not significantly vary with varying parameter values (P ≥ 0.09). The optimized combination of parameter values, identified as the one showing the highest AUC, was obtained for MNL = 3, MNI = 500 and MNC = 50 (AUC = 86 %; ANN structure: 3 hidden layers of 14, 14 and 13 neurons, respectively). Thus, RS&LP is robust, and the optimized ANN represents a potentially useful clinical tool for a reliable automatic HF diagnosis.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/306258

Assessment of electrical dyssynchrony in cardiac resynchronization therapy: 12-lead electrocardiogram vs. 96-lead body surface map
EUROPACE
Autore/i: Sedova, Ksenia A; van Dam, Peter M; Sbrollini, Agnese; Burattini, Laura; Necasova, Lucie; Blahova, Marie; Bocek, Jan; Sramko, Marek; Kautzner, Josef
Classificazione: 1 Contributo su Rivista
Abstract: Aims The standard deviation of activation time (SDAT) derived from body surface maps (BSMs) has been proposed as an optimal measure of electrical dyssynchrony in patients with cardiac resynchronization therapy (CRT). The goal of this study was two-fold: (i) to compare the values of SDAT in individual CRT patients with reconstructed myocardial metrics of depolarization heterogeneity using an inverse solution algorithm and (ii) to compare SDAT calculated from 96-lead BSM with a clinically easily applicable 12-lead electrocardiogram (ECG). Methods and results Cardiac resynchronization therapy patients with sinus rhythm and left bundle branch block at baseline (n = 19, 58% males, age 60 +/- 11 years, New York Heart Association Classes II and III, QRS 167 +/- 16) were studied using a 96-lead BSM. The activation time (AT) was automatically detected for each ECG lead, and SDAT was calculated using either 96 leads or standard 12 leads. Standard deviation of activation time was assessed in sinus rhythm and during six different pacing modes, including atrial pacing, sequential left or right ventricular, and biventricular pacing. Changes in SDAT calculated both from BSM and from 12-lead ECG corresponded to changes in reconstructed myocardial ATs. A high degree of reliability was found between SDAT values obtained from 12-lead ECG and BSM for different pacing modes, and the intraclass correlation coefficient varied between 0.78 and 0.96 (P < 0.001). Conclusion Standard deviation of activation time measurement from BSM correlated with reconstructed myocardial ATs, supporting its utility in the assessment of electrical dyssynchrony in CRT. Importantly, 12-lead ECG provided similar information as BSM. Further prospective studies are necessary to verify the clinical utility of SDAT from 12-lead ECG in larger patient cohorts, including those with ischaemic cardiomyopathy.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/309104

A Reliable User-Independent Motion Intent Detection from Transient EMG Data for Shoulder Joint
Convegno Nazionale di Bioingegneria
Autore/i: Scattolini, M.; Tigrini, A.; Verdini, F.; Fioretti, S.; Burattini, L.; Morettini, M.; Mengarelli, A.
Editore: Patron Editore S.r.l.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Motion intent detection (MID) through transient surface electromyographic (sEMG) is becoming central for the development of real-time assistive and rehabilitative technologies. However, the adaptation of such myoelectric interfaces in a user-independent framework represents a cutting-edge problem that hampers the practical usage of sEMG based human-machine interfaces. In this study least square canonical correlation analysis (LS-CCA) was employed together with SVM classifier in order to solve a shoulder joint MID problem. A publicly available dataset comprising eight healthy subjects was employed and four shoulder movements were considered. The LS-CCA was used for computing a subject-independent feature space using training data and then the trained SVM model was tested on an unseen subject, eventually implementing a leave-one-subject-out validation. Two window lengths for feature extraction (50 and 150 ms) and three feature sets were also compared. Overall, the best results were obtained using the 50 ms window length (multiuser classification accuracy about 70%), without any significant difference among the three feature sets.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/324353

Ratio Indexes Based on Spectral Electroencephalographic Brainwaves for Assessment of Mental Involvement: A Systematic Review
SENSORS
Autore/i: Marcantoni, Ilaria; Assogna, Raffaella; Del Borrello, Giulia; Di Stefano, Marina; Morano, Martina; Romagnoli, Sofia; Leoni, Chiara; Bruschi, Giulia; Sbrollini, Agnese; Morettini, Micaela; Burattini, Laura
Classificazione: 1 Contributo su Rivista
Abstract: This review systematically examined the scientific literature about electroencephalogram-derived ratio indexes used to assess human mental involvement, in order to deduce what they are, how they are defined and used, and what their best fields of application are. (2) Methods: The review was carried out according to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines. (3) Results: From the search query, 82 documents resulted. The majority (82%) were classified as related to mental strain, while 12% were classified as related to sensory and emotion aspects, and 6% to movement. The electroencephalographic electrode montage used was low-density in 13%, high-density in 6% and very-low-density in 81% of documents. The most used electrode positions for computation of involvement indexes were in the frontal and prefrontal cortex. Overall, 37 different formulations of involvement indexes were found. None of them could be directly related to a specific field of application. (4) Conclusions: Standardization in the definition of these indexes is missing, both in the considered frequency bands and in the exploited electrodes. Future research may focus on the development of indexes with a unique definition to monitor and characterize mental involvement.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/319611

Model-based Estimators of QT Series Time Delay in Following Heart-Rate Changes
Proceedings of the 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Autore/i: Romagnoli, S.; Perez, C.; Burattini, L.; Pueyo, E.; Morettini, M.; Sbrollini, A.; Martinez, J. P.; Laguna, P.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Sudden cardiac death is the leading cause of death among cardiovascular diseases. Markers for patient risk stratification focusing on QT-interval dynamics in response to heart-rate (HR) changes can be characterized in terms of parametric QT to RR dependence and QT/RR hysteresis. The QT/RR hysteresis can be quantified by the time delay the QT interval takes to accommodate for the HR changes. The exercise stress test has been proposed as a proper test, with large HR dynamics, to evaluate the QT/RR hysteresis. The present study aims at evaluating several time-delay estimators based on noise statistic (Gaussian or Laplacian) and HR changes profile at stress test (gradual transition change). The estimator's performance was assessed on a simulated QT transition contaminated by noise and in a clinical study including patients affected by coronary arteries disease (CAD). As expected, the Laplacian and Gaussian estimators yield the best results when noise follows the respective distribution. Further, the Laplacian estimator showed greater discriminative power in classifying different levels of cardiac risk in CAD patients, suggesting that real data fit better the Laplacian distribution than the Gaussian one. The Laplacian estimator appears to be the choice for time-delay estimation of QT/RR hysteresis lag in response to HR changes in stress test.Clinical Relevance-The proposed time-delay estimator of QT/RR hysteresis lag improves its significance as biomarkers for coronary artery diseases risk stratification.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/325438

Mathematical model of glucagon kinetics during an oral glucose tolerance test based on a dual regulation mechanism
Convegno Nazionale di Bioingegneria- GNB2023
Autore/i: Morettini, M.; Piersanti, A.; Gobl, C.; Burattini, L.; Tura, A.
Editore: Patron Editore S.r.l.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Glucagon is a hormone secreted by the pancreatic alpha cells and plays a key role in glucose homeostasis and in the pathophysiology of type 2 diabetes (T2D). Different mechanisms are involved in its regulation, but exact mechanisms are still largely unknown. This study aimed to propose a model describing glucagon inhibition during an oral glucose tolerance test (OGTT), accounting for a double regulation mechanism. The model has been developed starting from a model previously proposed by our group that includes two differential equations, one for plasma glucagon and one for C-peptide (marker of insulin at pancreatic level). In the new model, in addition to plasma C-peptide, plasma glucose is included as model input. The model provides two parameters of possible clinical relevance, namely SGLUCA and kG (alpha-cell insulin and glucose sensitivity, respectively) and has been validated on mean literature data of healthy subjects and subjects affected by T2D (CNT and T2D, respectively). Model analysis yielded SGLUCA estimates ranging from -0.1515 to 0.7629 and from -5.5602 to 1.1067 (ng of glucagon·nmol of C-peptide-1) in CNT and T2D groups, respectively; according to the 95% confidence intervals (CIs), SGLUCA was significantly different from zero in 4 and in 0 out of 8 time points, in CNT and T2D. Estimates for kG were equal to 2.8302 (95% CIs: 1.1973-4.4632) and 0.9913 (95% CIs: -0.5559-2.5386) ng of glucagon·mmol of glucose-1. Thus, results suggest both insulin (represented by C-peptide) and glucose significantly contributes to glucagon inhibition in healthy subjects, but not in T2D. In conclusion, the proposed model may help to describe different mechanisms acting on glucagon inhibition in the single individuals.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/324914

Brugada Syndrome: Characterization of QT Interval Components and Correction
Convegno Nazionale di Bioingegneria- GNB2023
Autore/i: Sbrollini, A.; Romagnoli, S.; Locati, E. T.; Morettini, M.; Burattini, L.
Editore: Patron Editore S.r.l.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Brugada syndrome (BrS) is an inherited cardiac disorder at high risk of sudden death. There are two ECG patterns for BrS: Type 1 (coved-type), representing the only diagnostic pattern for BrS (BrS1), while type 2 (saddle-back type) is only suggestive of BrS (BrS2). The QT interval on surface ECG, known to be heart-rate (HR) dependent, characterizes ventricular activity and is associated with the risk of ventricular arrhythmias. Aim of this study is to investigate the role of QT-interval and QT-interval-segment correction in subjects with BrS1 and BrS2, compared to controls (CTR). Open-access “Brugada ECG Database” by Zenodo was considered, containing HR, QT interval and its segments (QJ, JTp, and TpTe) of 60 subjects with BrS1, 27 subjects with BrS2 and 51 CTR. Time intervals and segments were corrected according to nine correction formulae. Feature distributions of BrS1, BrS2 and CTR were compared by unpaired Wilcoxon rank sum test. All BrS1 and BrS2 features were statistically different from CTR. QT interval and corrected QT interval were significantly longer in BrS1 and BrS2 than in CTR, mainly due to longer QJ and TpTe, while JTp was significantly shorter in BrS1 and BrS2 than in CTR. No significant differences were observed in QJ and JTp between BrS1 and BrS2, whereas five correction formulae revealed that BrS1 had significantly longer TpTe interval than BrS2, suggesting physiological differences between BrS1 and BrS2 in late repolarization phase. In conclusion, major differences exist between BrS patients and CRT in QT-interval duration, mainly due to longer QJ and shorter JTp duration in BrS patients, while differences may exist between BrS1 and BrS2 patients in the late repolarization phase, highlighting the need of proper QT-HR correction to better characterize BrS patients.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/324912

Wearable and Portable Devices for Acquisition of Cardiac Signals while Practicing Sport: A Scoping Review
SENSORS
Autore/i: Romagnoli, Sofia; Ripanti, Francesca; Morettini, Micaela; Burattini, Laura; Sbrollini, Agnese
Classificazione: 1 Contributo su Rivista
Abstract: Wearable and portable devices capable of acquiring cardiac signals are at the frontier of the sport industry. They are becoming increasingly popular for monitoring physiological parameters while practicing sport, given the advances in miniaturized technologies, powerful data, and signal processing applications. Data and signals acquired by these devices are increasingly used to monitor athletes' performances and thus to define risk indices for sport-related cardiac diseases, such as sudden cardiac death. This scoping review investigated commercial wearable and portable devices employed for cardiac signal monitoring during sport activity. A systematic search of the literature was conducted on PubMed, Scopus, and Web of Science. After study selection, a total of 35 studies were included in the review. The studies were categorized based on the application of wearable or portable devices in (1) validation studies, (2) clinical studies, and (3) development studies. The analysis revealed that standardized protocols for validating these technologies are necessary. Indeed, results obtained from the validation studies turned out to be heterogeneous and scarcely comparable, since the metrological characteristics reported were different. Moreover, the validation of several devices was carried out during different sport activities. Finally, results from clinical studies highlighted that wearable devices are crucial to improve athletes' performance and to prevent adverse cardiovascular events.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/313147

Handwritten Digits Recognition from sEMG: Electrodes Location and Feature Selection
IEEE ACCESS
Autore/i: Tigrini, A.; Verdini, F.; Scattolini, M.; Barbarossa, F.; Burattini, L.; Morettini, M.; Fioretti, S.; Mengarelli, A.
Classificazione: 1 Contributo su Rivista
Abstract: Objective: Despite hand gesture recognition is a widely investigated field, the design of myoelectric architectures for detecting finer motor task, like the handwriting, is less studied. However, writing tasks involving cognitive loads represent an important aspect toward the generalization of myoelectric-based human-machine interfaces (HMI), and also for many rehabilitative tasks. In this study, the handwriting recognition of the ten digits was faced under the myoelectric control perspective, considering the probes setup and the feature extraction step. Methods: Time and frequency domain features were extracted from surface electromyography (sEMG) signals of 11 subjects who wrote the ten digits following a standardized template and 8 sEMG probes were equally distributed between forearm and wrist. Feature class separability was investigated and an aggregated feature set was built to train pattern recognition architectures, i.e. linear discriminant analysis (LDA) and quadratic support vector machine (QSVM). Also, four reduced probes setups were investigated. Results: LDA and QSVM showed mean accuracy of about 97%, with all the forearm and wrist sEMG information. A significant reduction of performances was observed considering the wrist or the forearm only (≤92%) and when LDA and QSVM were trained with two electrodes information (≤90%). Conclusions: For the reliable classification performances in a motor task involving high cognitive demands, like the handwriting, it is required the use of probes fully covering forearm and wrist. Outcomes support the methodological transfer from myoelectric hand gesture to the handwriting recognition, which represents a key aspect in the development of new HMI for rehabilitation tasks.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/318071

Physiological and Biomechanical Monitoring in American Football Players: A Scoping Review
SENSORS
Autore/i: Nocera, A.; Sbrollini, A.; Romagnoli, S.; Morettini, M.; Gambi, E.; Burattini, L.
Classificazione: 1 Contributo su Rivista
Abstract: American football is the sport with the highest rates of concussion injuries. Biomedical engineering applications may support athletes in monitoring their injuries, evaluating the effectiveness of their equipment, and leading industrial research in this sport. This literature review aims to report on the applications of biomedical engineering research in American football, highlighting the main trends and gaps. The review followed the PRISMA guidelines and gathered a total of 1629 records from PubMed (n = 368), Web of Science (n = 665), and Scopus (n = 596). The records were analyzed, tabulated, and clustered in topics. In total, 112 studies were selected and divided by topic in the biomechanics of concussion (n = 55), biomechanics of footwear (n = 6), biomechanics of sport-related movements (n = 6), the aerodynamics of football and catch (n = 3), injury prediction (n = 8), heat monitoring of physiological parameters (n = 8), and monitoring of the training load (n = 25). The safety of players has fueled most of the research that has led to innovations in helmet and footwear design, as well as improvements in the understanding and prevention of injuries and heat monitoring. The other important motivator for research is the improvement of performance, which has led to the monitoring of training loads and catches, and studies on the aerodynamics of football. The main gaps found in the literature were regarding the monitoring of internal loads and the innovation of shoulder pads.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/313789

A Machine-Learning Framework based on Continuous Glucose Monitoring to Prevent the Occurrence of Exercise-Induced Hypoglycemia in Children with Type 1 Diabetes
Proceedings - IEEE Symposium on Computer-Based Medical Systems
Autore/i: Piersanti, A.; Salvatori, B.; Gobl, C.; Burattini, L.; Tura, A.; Morettini, M.
Editore: Institute of Electrical and Electronics Engineers Inc.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Physical activity is recommended in patients with type 1 diabetes (T1D), but therapy management still lacks efficient tools to avoid exercise-induced hypoglycemia. Machine learning represents a powerful solution in the field of decision support for diabetes management and its application to continuous glucose monitoring (CGM) data appears promising in pre-exercise prediction of upcoming adverse events. Aim of this study was to investigate the possibility to distinguish if a specific configuration of CGM metrics evaluated before starting of exercise is more prone to induce hypoglycemia after the start of the exercise session until the following day. A total of 47 CGM recordings from T1D children have been used to extract CGM metrics from pre-exercise CGM data. Acquisitions were labelled as HYPO or as NO-HYPO, respectively if belonging to subjects who experienced or did not experience hypoglycemia during the time following the exercise. Anthropometric characteristics and extracted features have been given as input to a decision tree classification algorithm to select those with the most predictive power. The selected features were then further evaluated with respect to the classification problem by using them as input to other three classification models: random forest, adaboost and gradient boosting. Performance results in terms of area under receiver operating characteristic (AUC) were as follows: 85.5%, 82.1%, 78.1% and 74.3% for decision tree, gradient boosting, random forest and adaboost, respectively. M-value, maximum glucose, time above 180 mg/dL and time above 250 mg/dL could have a role in predicting upcoming hypoglycemia prior the starting of exercise.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/320314

Interhemispheric functional connectivity: an fMRI study in two split-brain patients
Convegno Nazionale di Bioingegneria- GNB2023
Autore/i: Marcantoni, I.; Piccolantonio, G.; Vitti, E.; Polonara, G.; Ghoushi, M.; Valenti, M.; Reversi, L.; Foschi, N.; Lattanzi, S.; Burattini, L.; Fabri, M.
Editore: Patron Editore S.r.l.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Resting state networks (RSNs) were most studied using functional magnetic resonance imaging (fMRI). Interhemispheric functional connectivity (FC) appears mostly supported by the corpus callosum (CC) and several studies have investigated this aspect, still not providing conclusive evidence. In this context, patients in whom the CC was resected for therapeutic reasons (split-brain patients) provide a unique opportunity of research. The purpose of this pilot study is to investigate, with resting state fMRI, the interhemispheric FC in two callosotomized patients operated in adult age, and in four healthy control subjects with intact brain. The analysis was performed with fMRI of the Brain (FMRIB) Software Library (FSL); the evaluation of interhemispheric FC and the recognition of the RSNs were performed by using the probabilistic independent component analysis (PICA). The multi-subject analysis on control subjects allowed the identification of three RSNs: medial visual, default mode, and sensory motor. Both patients showed a bilateral brain activation in the medial visual network, comparable with the controls; in the sensory motor and the default mode networks the activation was unilateral, at variance with controls. These results seem to suggest that the brain, during its maturation, builds new subcortical communication pathways, alternative to the CC, which might support interhemispheric FC in case of callosotomy. Further studies are needed to confirm the observations obtained and reported in this preliminary research.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/324913

mmWave Radars Data Processing for Gait Parameters Extraction
Convegno Nazionale di Bioingegneria- GNB2023
Autore/i: Nocera, A.; Verdini, F.; Fioretti, S.; Ciattaglia, G.; Raimondi, M.; Burattini, L.; Senigagliesi, L.; Gambi, E.
Editore: Patron Editore S.r.l.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: A person's mobility can be examined to learn vital details about their health.Non-contact technology can be used for this investigation with the aim of ensuring freedom of movement of the subject and realistic analysis of movement. Automotive mmWaves radars, while made for different uses, are a powerful low-cost technology for detecting human movement without physical contact, which can introduce fascinating implications as an aid to health monitoring at home or at work. This paper shows how to process the data recorded by commercial radars to extract simple spatio-temporal parameters of people's walking. The processing algorithms provides the cadence, step length and step duration with a good approximation of the true gait parameters.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/324911

On-cloud decision-support system for non-small cell lung cancer histology characterization from thorax computed tomography scans
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
Autore/i: Tomassini, Selene; Falcionelli, Nicola; Bruschi, Giulia; Sbrollini, Agnese; Marini, Niccolò; Sernani, Paolo; Morettini, Micaela; Müller, Henning; Dragoni, Aldo Franco; Burattini, Laura
Classificazione: 1 Contributo su Rivista
Abstract: Non-Small Cell Lung Cancer (NSCLC) accounts for about 85% of all lung cancers. Developing non-invasive techniques for NSCLC histology characterization may not only help clinicians to make targeted therapeutic treatments but also prevent subjects from undergoing lung biopsy, which is challenging and could lead to clinical implications. The motivation behind the study presented here is to develop an advanced on-cloud decisionsupport system, named LUCY, for non-small cell LUng Cancer histologY characterization directly from thorax Computed Tomography (CT) scans. This aim was pursued by selecting thorax CT scans of 182 LUng ADenocarcinoma (LUAD) and 186 LUng Squamous Cell carcinoma (LUSC) subjects from four openly accessible data collections (NSCLC-Radiomics, NSCLC-Radiogenomics, NSCLC-Radiomics-Genomics and TCGA-LUAD), in addition to the implementation and comparison of two end-to-end neural networks (the core layer of whom is a convolutional long short-term memory layer), the performance evaluation on test dataset (NSCLC-RadiomicsGenomics) from a subject-level perspective in relation to NSCLC histological subtype location and grade, and the dynamic visual interpretation of the achieved results by producing and analyzing one heatmap video for each scan. LUCY reached test Area Under the receiver operating characteristic Curve (AUC) values above 77% in all NSCLC histological subtype location and grade groups, and a best AUC value of 97% on the entire dataset reserved for testing, proving high generalizability to heterogeneous data and robustness. Thus, LUCY is a clinically-useful decision-support system able to timely, non-invasively and reliably provide visuallyunderstandable predictions on LUAD and LUSC subjects in relation to clinically-relevant information.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/325437

Is T-Wave Alternans a Repolarization Abnormality Marker in COVID-19? An Investigation on the Potentialities of Portable Electrocardiogram Device
CARDIOLOGY RESEARCH
Autore/i: Tondas, Alexander Edo; Munawar, Dian Andina; Marcantoni, Ilaria; Liberty, Iche Andriyani; Mulawarman, Rido; Hadi, Muhammad; Trifitriana, Monica; Indrajaya, Taufik; Yamin, Muhammad; Irfannuddin, Irfannuddin; Burattini, Laura
Classificazione: 1 Contributo su Rivista
Abstract: Background: Cardiac arrhythmias are significantly associated with poor outcomes in coronavirus disease 2019 (COVID-19) patients. Microvolt T-wave alternans (TWA) can be automatically quantified and has been recognized as a representation of repolarization hetero-geneity and linked to arrhythmogenesis in various cardiovascular dis-eases. This study aimed to explore the correlation between microvolt TWA and COVID-19 pathology. Methods: Patients suspected of COVID-19 in Mohammad Hoesin General Hospital were consecutively evaluated using Alivecor (R) Kar-diamobile 6LTM portable electrocardiogram (ECG) device. Severe COVID-19 patients or those who are unable to cooperate in active ECG self-recording were excluded from the study. TWA was detected and its amplitude was quantified using the novel enhanced adaptive match filter (EAMF) method. Results: A total of 175 patients, 114 COVID-19 patients (polymer-ase chain reaction (PCR)-positive group), and 61 non-COVID-19 pa -tients (PCR-negative group) were enrolled in the study. PCR-positive group was subdivided according to the severity of COVID-19 pathol-ogy into mild and moderate severity subgroups. Baseline TWA levels were similar between both groups during admission (42.47 +/- 26.52 mu V vs. 44.72 +/- 38.21 mu V), but higher TWA levels were observed during discharge in the PCR-positive compared to the PCR-negative group (53.45 +/- 34.42 mu V vs. 25.15 +/- 17.64 mu V, P = 0.03). The correlation between PCR-positive result in COVID-19 and TWA value was sig-nificant, after adjustment of other confounding variables (R2 = 0.081, P = 0.030). There was no significant difference in TWA levels between mild and moderate severity subgroups in patients with COVID-19, both during admission (44.29 +/- 27.14 mu V vs. 36.75 +/- 24.46 mu V, P = 0.34) and discharge (49.47 +/- 33.62 mu V vs. 61.09 +/- 35.99 mu V, P = 0.33). Conclusions: Higher TWA values can be observed on follow-up ECG obtained during discharge in the PCR-positive COVID-19 patients.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/315209

Spatial distribution of BOLD activations evoked by three different tastants to build a chemotopic map of primary gustatory area: A pilot study
Proceedings - IEEE Symposium on Computer-Based Medical Systems
Autore/i: Marcantoni, I.; Tomaiuolo, F.; Piccolantonio, G.; Sbrollini, A.; Morettini, M.; Fiori, F.; Vignini, A.; Polonara, G.; Burattini, L.; Fabri, M.
Editore: Institute of Electrical and Electronics Engineers Inc.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: The gustatory areas of the brain include the primary (GI) and the secondary (GII) gustatory cerebral cortex. A spatial mechanism has been recently hypothesized to explain the taste quality recognition. This research investigates the spatial distribution of blood oxygen level dependent (BOLD) activations evoked in the human area GI by different tastants, aimed at building a chemotopic map. The chemotopic organization of the human GI was studied in seven healthy subjects by applying three taste stimuli (salty, sweet, neutral) to either side of the tongue, using a 5-min functional magnetic resonance imaging (fMRI) block-designed protocol, alternating periods of rest and stimulation. Data were analyzed by the Functional Magnetic Resonance Imaging of the Brain (FMRIB) Software Library (FSL). Unilateral tongue stimulation consistently evoked bilateral activation in area GI. Ipsilateral foci were generally larger and signal increases greater. The foci evoked by each tastant exhibited slightly but not significantly different mean coordinates, broad overlap and high interindividual variability; the salty stimuli generally evoked more anterior foci and sweet stimuli more posterior activation. Results confirm that the gustatory pathways from tongue to cortex are bilaterally distributed, with an ipsilateral predominance. Although distinct GI zones were activated by the different taste stimuli, a clear topographical organization could not be recognized. Possibly, fMRI technique is unable to resolve fine topographical arrangements or GI discriminative role for different tastants is subserved by another mechanism. Bilateral activation of the primary somatosensory area in the parietal cortex (contralateral predominance) and of middle insula (ipsilateral predominance) were also observed.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/320315

CLAUDIA: Cloud-based Automatic Diagnosis of Alzheimer's Prodromal Stage and Disease from 3D Brain Magnetic Resonance
Proceedings - IEEE Symposium on Computer-Based Medical Systems
Autore/i: Tomassini, S.; Sbrollini, A.; Morettini, M.; Dragoni, A. F.; Burattini, L.
Editore: Institute of Electrical and Electronics Engineers Inc.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Alzheimer's Disease (AD) is the most common neurodegenerative disease. Its first stage, namely prodromal or Mild Cognitive Impairment (MCI), is characterized by slightly structural changes in the subcortical structures of the temporal lobe. Brain Magnetic Resonance (MR) is the most utilized neu-roimaging modality for the diagnosis of AD. Although an early therapeutic intervention during the initial stages of AD appears to have a positive impact on the progression of symptoms, its accurate diagnosis is still very difficult. Deep Learning (DL)-based decision-support systems hold great potential in generalizing even under subtle anatomical changes of the brain, like the ones caused by AD at its onset. To our knowledge, we were the first to develop a Convolutional Long Short-Term Memory (ConvLSTM)-based decision-support system and an improved version of it for the automatic diagnosis of AD from 3D brain MR. The research presented in this paper aims to extend their applicability to MCI for effectiveness verification through the development of CLAUDIA, a new on-cloud decision-support system for the automatic diagnosis of Alzheimer's prodromal stage and disease from 3D brain MR. To this aim, we selected 438 unenhanced scans from the ADNI-1 dataset, preprocessed them, and injected the preprocessed scans to the ConvLSTM-based neural network for automatic feature extraction and binary/multiclass classification. On test data, CLAUDIA achieved very encouraging results that highlight the superiority of the multiclass classifier in comparison to the two binary classifiers. On the basis of the achieved outcomes, we demonstrated that CLAUDIA, being the first to extend the applicability of a ConvLSTM-based neural network to MCI for effectiveness verification, represents a promising scan-, DL-based decision-support system for the automatic diagnosis of Alzheimer's prodromal stage and disease from 3D brain MR. Moreover, its cloud thus machine-independent nature ensures a full reproducibility of the implementation while guaranteeing cost saving and sustainability.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/320312

Toward a Minimal sEMG Setup for Knee and Ankle Kinematic Estimation during Gait
Proceedings - IEEE Symposium on Computer-Based Medical Systems
Autore/i: Mengarelli, A.; Verdini, F.; Al-Timemy, A. H.; Mobarak, R.; Scattolini, M.; Fioretti, S.; Burattini, L.; Tigrini, A.
Editore: Institute of Electrical and Electronics Engineers Inc.
Classificazione: 2 Contributo in Volume
Abstract: Modern rehabilitation and assistive devices require the use of smart interfaces able to capture the subject's intent of motion and translate such intent to specific control strategies. The use of surface electromyography (sEMG) signals, in combination with data-driven models, constitutes a viable framework to solve the aforementioned problem. Although literature highlighted the tendency toward a multiple sensors approach, a minimal set-up may reduce costs and complexity of myoelectric interfaces. In this study, gastrocnemius lateralis (GAL) and tibialis anterior (TA) sEMG signals were used in order to investigate their single and combined role in the flexion-extension angles estimation of ankle and knee during gait. Least-square support vector machine (LS-SVM) with linear, polynomial, and radial basis function (RBF) kernel was employed to estimate the most suitable function that maps the myoelectric information from single muscle and from the combination of both, in lower limbs joint kinematics. LS-SVM with RBF outperformed the other kernels in the ankle and knee kinematics estimation for all the 6 subjects examined. Moreover, when using RBF with the only GAL data, the median root mean square error (RMSE) values were above 5 degrees for ankle and 8 degrees for knee angles whereas the combined information from GAL and TA showed slightly better results. Outcomes support a minimal electrodes set-up for the development of lower limb myoelectric interfaces for kinematic estimation.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/320311

Symbolic Analysis of Heart-Rate Variability during Training and Competition in Short Distance Running
Proceedings - IEEE Symposium on Computer-Based Medical Systems
Autore/i: Romagnoli, S.; Sbrollini, A.; Morettini, M.; Burattini, L.
Editore: Institute of Electrical and Electronics Engineers Inc.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: During physical exercise, the assessment of cardiac autonomic regulation through the analysis of heart-rate variability is difficult due to non-stationarity of RR intervals. The short duration of stationary epoch of RR-interval series makes not possible the computation of classical time-and frequency-domain parameters. Symbolic analysis can deal with short epoch of RR interval; thus, this study aims to apply symbolic analysis to analyze heart-rate variability of short-distance runners during training and competition. Data consists of RR-intervals extracted from 30s-long electrocardiograms acquired by KardiaMobile of Alivecor in 8 short-distance runners (1/7 M/F, 17[16;20] years) during training and competition. Symbolic analysis classifies a reduced number (in this study, three) of consecutive RR intervals in four patterns according to the sign and number of variations: no variation (0V); one variation (1V); two like variations (2LV); two unlike variations (2UV). An increase in low amount of variations (0V or 1V patterns) is usually linked with increased sympathetic control and vagal withdrawal, while an increase in high amount of variations (2LV and 2UV patterns) is usually linked with sympathetic withdrawal and increased vagal control. In our results, pattern 0V /2LV increases/decreases from rest to post exercise and decreases/increases during recovery, in both training and competition. Thus, our results confirm the opposite trends between low and high variations of symbolic patterns. In conclusion, symbolic analysis seems to be an efficient tool to characterize heart-rate variability during physical exercise at different level of psychophysical stress.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/320313

Decoding transient sEMG data for intent motion recognition in transhumeral amputees
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
Autore/i: Tigrini, A.; Al-Timemy, A. H.; Verdini, F.; Fioretti, S.; Morettini, M.; Burattini, L.; Mengarelli, A.
Classificazione: 1 Contributo su Rivista
Abstract: The use of surface electromyographic (sEMG) signals, alongside pattern recognition (PR) systems, is fundamental in the design and control of assistive technologies. Transient sEMG signal epochs at the early beginning of the movement provide important information for upper-limb intent of motion recognition. However, only few studies investigated the role of transient sEMG for myoelectric control architectures. Therefore, in this work, focus was given to transient sEMG signals of intact-limb (IL) subjects and transhumeral amputees (AMP), who performed a series of shoulder movements. The role of the window length for feature extraction was investigated by sub-windowing the transient epochs at 200, 150, 100, and 50 ms window length (WL). Gaussian kernel discriminant analysis (SRKDA) and support vector machine (SVM) were used for recognizing seven classes of motion at different hold-out percentage of training/testing data, i.e. 70%–30%, 60%–40% and 50%–50%. In all the latter conditions, the median classification accuracy and F1 score were greater than 80% for both IL and AMP groups when using SRKDA. Wilcoxon rank sum test was employed to verify possible differences between WL conditions. Although the latter did not show significant differences, 100 ms WL showed the best classification performances for both groups (classification accuracy greater than 90%, near that of a usable PR system). Results demonstrated that a reliable motion intent recognition of shoulder joint in transhumeral amputee patients can be obtained employing transient sEMG epochs. This can be used in a better design of myoelectric control architectures of assistive technologies, involving the upper-limb for clinical use.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/313727

Gait Event Timeseries Assessment through Spectral Biomarkers and Machine Learning
Proceedings - IEEE Symposium on Computer-Based Medical Systems
Autore/i: Tigrini, A.; Verdini, F.; Fioretti, S.; Scattolini, M.; Mobarak, R.; Gambi, E.; Burattini, L.; Mengarelli, A.
Editore: Institute of Electrical and Electronics Engineers Inc.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: The study of motor disorders due to neurodegenerative diseases (NDD) is assuming a central role in healthcare systems, this is certainly due to the needs of early recognition systems that can allow a better management of the patients daily-life. Many studies in the literature faced the problem of finding digital biomarkers from data collected through gait experiments to discriminate between control (CN) and NDD groups without systematically face the problem of which gait time-series were more appropriate to extract opportune descriptors for characterizing the NDD considered. In this work, such problem was modeled through a machine learning approach. Thus, 6 time-dependent spectral features (PSDTD) were extracted from 4 gait time-series, i.e., stride (SR), stance (SA), swing (SW) and double support (DS) duration intervals. A publicly available data set containing data of CN, Parkinson's (PD), Huntington's (HD) and amyotrophic lateral sclerosis (ALS) diseases was employed to the purpose. Low error rates using leave one out validation scheme were obtained using PSDTD features computed over DS and SA for CN-PD and CN-HD classification, i.e., error rate < 0.1 for DS and < 0.15 for SA. Regarding CN-ALS classification, best results were obtained using SA features, i.e. error rate <0.07. This supports the research line that dynamic equilibrium phases of the gait can hide important biomarkers for the characterization of different NDD.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/320291

Segmentation of Anatomical Structures of the Left Heart from Echocardiographic Images Using Deep Learning
DIAGNOSTICS
Autore/i: Mortada, Mhd Jafar; Tomassini, Selene; Anbar, Haidar; Morettini, Micaela; Burattini, Laura; Sbrollini, Agnese
Classificazione: 1 Contributo su Rivista
Abstract: Knowledge about the anatomical structures of the left heart, specifically the atrium (LA) and ventricle (i.e., endocardium-Vendo-and epicardium-LVepi) is essential for the evaluation of cardiac functionality. Manual segmentation of cardiac structures from echocardiography is the baseline reference, but results are user-dependent and time-consuming. With the aim of supporting clinical practice, this paper presents a new deep-learning (DL)-based tool for segmenting anatomical structures of the left heart from echocardiographic images. Specifically, it was designed as a combination of two convolutional neural networks, the YOLOv7 algorithm and a U-Net, and it aims to automatically segment an echocardiographic image into LVendo, LVepi and LA. The DL-based tool was trained and tested on the Cardiac Acquisitions for Multi-Structure Ultrasound Segmentation (CAMUS) dataset of the University Hospital of St. Etienne, which consists of echocardiographic images from 450 patients. For each patient, apical two- and four-chamber views at end-systole and end-diastole were acquired and annotated by clinicians. Globally, our DL-based tool was able to segment LVendo, LVepi and LA, providing Dice similarity coefficients equal to 92.63%, 85.59%, and 87.57%, respectively. In conclusion, the presented DL-based tool proved to be reliable in automatically segmenting the anatomical structures of the left heart and supporting the cardiological clinical practice.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/316996

DL-LVEF: Deep-Learning Measurement of the Left Ventricular Ejection Fraction from Echocardiographic Images
Computing in Cardiology
Autore/i: Sbrollini, A.; Mortada, M. H. D. J.; Tomassini, S.; Anbar, H.; Morettini, M.; Burattini, L.
Editore: IEEE Computer Society
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Left ventricular ejection fraction (LVEF) is a commonly used index of cardiac functionality. Thus, accuracy in its measurement is fundamental. LVEF measure is usually manually performed by clinicians from echocardiographic images. Use of automatic algorithms could make LVEF measurement more objective. Thus, the aim of the present work is to present DL-LVEF, a new automatic algorithm for LVEF measurement based on deep learning identification and segmentation of the left ventricular endocardium performed by combining the YOLOv7 algorithm and a U-Net. To this aim, the CAMUS database was used, which includes 1800 echocardiographic images acquired from 450 patients with annotated LVEF values and manual segmentation of the left ventricular endocardium. The database was divided into training dataset (70%) and testing dataset (30%). In both datasets, measured and annotated LVEF values (%) were found to be highly correlated (p=0.96 and p=0.89, respectively) and not statistically different (52.6% vs. 52.6% and 54.6% vs. 53.9%, respectively); mean absolute error was 4% and 5%, respectively. Thus, DL-LVEF provided objective and accurate LVEF measurement. Future DL-LVEF evolutions will also provide segmentation of other cardiac anatomical structures and, thus, will allow measurement of other clinically relevant cardiac indexes.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/326411

Sport DB 2.0: A New Database of Data Acquired by Wearable and Portable Devices While Practicing Sport
Computing in Cardiology
Autore/i: Romagnoli, S.; Sbrollini, A.; Nocera, A.; Morettini, M.; Gambi, E.; Bondi, D.; Pietrangelo, T.; Verratti, V.; Burattini, L.
Editore: IEEE Computer Society
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Sport DB 2.0 is a collection of 168 cardiorespiratory datasets, acquired through wearable sensors and portable devices from 130 subjects while practicing 11 different sports during training and competition. Each dataset consists of demographic data (sex, age, weight, height, smoking habit, alcohol consumption, caffeine consumption, weekly training rate, presence of diseases and dietary supplement consumption), cardiorespiratory signals (electrocardiogram, heart-rate series, RR-interval series, and/or breathing-rate series), and training note data (sport-dependent training protocol). Cardiorespiratory signals were acquired through the BioHarness 3.0 by Zephyr, the KardiaMobile by AliveCor, the Kardia 6L by AliveCor, the Polar M400 by Polar, and heart-rate sensor H7 by Polar, on the playing field or gym following a specific acquisition protocol for each sport. Sport DB 2.0 may be useful to support research activity finalized to investigate the cardiorespiratory pathophysiological mechanisms triggered by sport, to develop automatic algorithms for monitoring athletes' health while practicing sports, to validate the reliability of wearable sensors and portable devices in sport, and to develop data analytics techniques and artificial intelligence applications to support sport sciences.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/326419

Identification of Respiration Types Through Respiratory Signal Derived from Clinical and Wearable Electrocardiograms
IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY
Autore/i: Sbrollini, A.; Morettini, M.; Gambi, E.; Burattini, L.
Classificazione: 1 Contributo su Rivista
Abstract: Goal: To evaluate suitability of respiratory signals derived from clinical 12-lead electrocardiograms (ECGs) and wearable 1-lead ECG to identify different respiration types. Methods: ECGs were simultaneously acquired through the M12R ECG Holter by Global Instrumentation and the chest strap BioHarness 3.0 by Zephyr from 42 healthy subjects alternating normal breathing, breath holding, and deep breathing. Respiration signals were derived from the ECGs through the Segmented-Beat Modulation Method (SBMM)-based algorithm and the algorithms by Van Gent, Charlton, Soni and Sarkar, and characterized in terms of breathing rate and amplitude. Respiration classification was performed through a linear support vector machine and evaluated by F1 score. Results: Best F1 scores were 86.59%(lead V2) and 80.57%, when considering 12-lead and 1-lead ECGs, respectively, and using SBMM-based algorithm. Conclusion: ECG-derived respiratory signals allow reliable identification of different respiration types even when acquired through wearable sensors, if associated to appropriate processing algorithms, such as the SBMM-based algorithm.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/325571

A Double-Stage 3D U-Net for On-Cloud Brain Extraction and Multi-Structure Segmentation from 7T MR Volumes
INFORMATION
Autore/i: Tomassini, S.; Anbar, H.; Sbrollini, A.; Mortada, M. H. D. J.; Burattini, L.; Morettini, M.
Classificazione: 1 Contributo su Rivista
Abstract: The brain is the organ most studied using Magnetic Resonance (MR). The emergence of 7T scanners has increased MR imaging resolution to a sub-millimeter level. However, there is a lack of automatic segmentation techniques for 7T MR volumes. This research aims to develop a novel deep learning-based algorithm for on-cloud brain extraction and multi-structure segmentation from unenhanced 7T MR volumes. To this aim, a double-stage 3D U-Net was implemented in a cloud service, directing its first stage to the automatic extraction of the brain and its second stage to the automatic segmentation of the grey matter, basal ganglia, white matter, ventricles, cerebellum, and brain stem. The training was performed on the 90% (the 10% of which served for validation) and the test on the 10% of the Glasgow database. A mean test Dice Similarity Coefficient (DSC) of 96.33% was achieved for the brain class. Mean test DSCs of 90.24%, 87.55%, 93.82%, 85.77%, 91.53%, and 89.95% were achieved for the brain structure classes, respectively. Therefore, the proposed double-stage 3D U-Net is effective in brain extraction and multi-structure segmentation from 7T MR volumes without any preprocessing and training data augmentation strategy while ensuring its machine-independent reproducibility.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/316995

Advanced repeated structuring and learning procedure to detect acute myocardial ischemia in serial 12-lead ECGs
PHYSIOLOGICAL MEASUREMENT
Autore/i: Sbrollini, A.; Ter Haar, C. C.; Leoni, C.; Morettini, M.; Burattini, L.; Swenne, C. A.
Classificazione: 1 Contributo su Rivista
Abstract: Objectives. Acute myocardial ischemia in the setting of acute coronary syndrome (ACS) may lead to myocardial infarction. Therefore, timely decisions, already in the pre-hospital phase, are crucial to preserving cardiac function as much as possible. Serial electrocardiography, a comparison of the acute electrocardiogram with a previously recorded (reference) ECG of the same patient, aids in identifying ischemia-induced electrocardiographic changes by correcting for interindividual ECG variability. Recently, the combination of deep learning and serial electrocardiography provided promising results in detecting emerging cardiac diseases; thus, the aim of our current study is the application of our novel Advanced Repeated Structuring and Learning Procedure (AdvRS&LP), specifically designed for acute myocardial ischemia detection in the pre-hospital phase by using serial ECG features.Approach. Data belong to the SUBTRACT study, which includes 1425 ECG pairs, 194 (14%) ACS patients, and 1035 (73%) controls. Each ECG pair was characterized by 28 serial features that, with sex and age, constituted the inputs of the AdvRS&LP, an automatic constructive procedure for creating supervised neural networks (NN). We created 100 NNs to compensate for statistical fluctuations due to random data divisions of a limited dataset. We compared the performance of the obtained NNs to a logistic regression (LR) procedure and the Glasgow program (Uni-G) in terms of area-under-the-curve (AUC) of the receiver-operating-characteristic curve, sensitivity (SE), and specificity (SP).Main Results. NNs (median AUC = 83%, median SE = 77%, and median SP = 89%) presented a statistically (Pvalue lower than 0.05) higher testing performance than those presented by LR (median AUC = 80%, median SE = 67%, and median SP = 81%) and by the Uni-G algorithm (median SE = 72% and median SP = 82%).Significance. In conclusion, the positive results underscore the value of serial ECG comparison in ischemia detection, and NNs created by AdvRS&LP seem to be reliable tools in terms of generalization and clinical applicability.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/321191

Leukocyte classification for acute lymphoblastic leukemia timely diagnosis by interpretable artificial neural network
Journal of Autonomous Intelligence
Autore/i: Sbrollini, A.; Tomassini, S.; Sharaan, R.; Morettini, M.; Dragoni, A. F.; Burattini, L.
Classificazione: 1 Contributo su Rivista
Abstract: Leukemia is a blood cancer characterized by leukocyte overproduction. Clinically, the reference for acute lymphoblastic leukemia diagnosis is a blood biopsy that allows obtain microscopic images of leukocytes, whose early-stage classification into leukemic (LEU) and healthy (HEA) may be disease predictor. Thus, the aim of this study is to propose an interpretable artificial neural network (ANN) for leukocyte classification to timely diagnose acute lymphoblastic leukemia. The “ALL_IDB2” dataset was used. It contains 260 microscopic images showing leukocytes acquired from 130 LEU and 130 HEA subjects. Each microscopic image shows a single leukocyte that was characterized by 8 morphological and 4 statistical features. An ANN was developed to distinguish microscopic images acquired from LEU and HEA subjects, considering 12 features as inputs and the local-interpretable model-agnostic explanatory (LIME) algorithm as an interpretable post-processing algorithm. The ANN was evaluated by the leave-one-out cross-validation procedure. The performance of our ANN is promising, presenting a testing area under the curve of the receiver operating characteristic equal to 87%. Being implemented using standard features and having LIME as a post-processing algorithm, it is clinically interpretable. Therefore, our ANN seems to be a reliable instrument for leukocyte classification to timely diagnose acute lymphoblastic leukemia, guaranteeing a high clinical interpretability level.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/320131

Robot Perception through Wearable Sensors: Decoding Grasping for Human-Robot Hand-Over
2022 I-RIM Conference
Autore/i: Bonci, Andrea; Burattini, Laura; Fioretti, Sandro; Giannini, MARIA CRISTINA; Longhi, Sauro; Mengarelli, Alessandro; Tigrini, Andrea; Verdini, Federica
Editore: I-RIM
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Human-robot interaction represents the cornerstone for the full development of Industry 4.0 and 5.0 paradigms, that rely on this cooperation in order to develop more efficient and flexible production lines. In this context, the human-robot handover plays a crucial role and many approaches were introduced to plan and control this task, including the less investigated decoding of human muscles activity. Hence, the design of reliable myoelectric human-robot interfaces is a point of primary interest. This paper investigates the use of a wearable device, i.e. an armband, for achieving a robust detection of several human grasping gestures. An evaluation of the most useful features, belonging to three different computational domains, is also proposed. Outcomes showed that high recognition performance can be achieved with limited computational burden, which is crucial when dealing with real-time demands in collaborative task.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/315772

ECG diagnosis by a multiclass neural network
Journal of Electrocardiology
Autore/i: Sbrollini, Agnese; Mancinelli, Martina; Leoni, Chiara; Marcantoni, Ilaria; Morettini, Micaela; Swenne, Cees A.; Burattini, Laura
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/313187

Mathematical model of insulin kinetics accounting for the amino acids effect during a mixed meal tolerance test
FRONTIERS IN ENDOCRINOLOGY
Autore/i: Morettini, Micaela; Palumbo, Maria Concetta; Göbl, Christian; Burattini, Laura; Karusheva, Yanislava; Roden, Michael; Pacini, Giovanni; Tura, Andrea
Classificazione: 1 Contributo su Rivista
Abstract: Amino acids (AAs) are well known to be involved in the regulation of glucose metabolism and, in particular, of insulin secretion. However, the effects of different AAs on insulin release and kinetics have not been completely elucidated. The aim of this study was to propose a mathematical model that includes the effect of AAs on insulin kinetics during a mixed meal tolerance test. To this aim, five different models were proposed and compared. Validation was performed using average data, derived from the scientific literature, regarding subjects with normal glucose tolerance (CNT) and with type 2 diabetes (T2D). From the average data of the CNT and T2D people, data for two virtual populations (100 for each group) were generated for further model validation. Among the five proposed models, a simple model including one first-order differential equation showed the best results in terms of model performance (best compromise between model structure parsimony, estimated parameters plausibility, and data fit accuracy). With regard to the contribution of AAs to insulin appearance/disappearance (kAA model parameter), model analysis of the average data from the literature yielded 0.0247 (confidence interval, CI: 0.0168 - 0.0325) and -0.0048 (CI: -0.0281 - 0.0185) μU·ml-1/(μmol·l-1·min), for CNT and T2D, respectively. This suggests a positive effect of AAs on insulin secretion in CNT, and negligible effect in T2D. In conclusion, a simple model, including single first-order differential equation, may help to describe the possible AAs effects on insulin kinetics during a physiological metabolic test, and provide parameters that can be assessed in the single individuals.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/306981

Role of the Window Length for Myoelectric Pattern Recognition in Detecting User Intent of Motion
Proceeding of 17th IEEE International Symposium on Medical Measurements and Applications
Autore/i: Tigrini, A.; Scattolini, M.; Mengarelli, A.; Fioretti, S.; Morettini, M.; Burattini, L.; Verdini, F.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In this study, the problem of electromyographic (EMG) based motion intention detection (MID) of upper limb was addressed, investigating the role of the window length for feature extraction. Two pattern recognition experiments were performed taking into account eight healthy subjects. The first involved the direct comparison of classification performances using feature computed over 150, 100 and 50 ms window length for eight class of shoulder movements. In the second one, a feature fusing scheme, based on canonical correlation analysis (CCA), was used to investigate whether pattern recognition architectures (PRAs), i.e. support vector machine, were able to boost their performances when 50 ms features were used as testing set. The rationale behind such investigations grounds on the lack of consensus regarding the most suitable window length for myoelectic pattern recognition. No drop of accuracy was observed in the first experiment for the three different windows length, maintaining values around 90%. Moreover, as observed in the second experiment, the CCA feature fusing scheme enhanced the performances of the PRAs when working over 50 ms features, reaching comparable results with feature at 150 ms. The proposed approach can be suitable for MID in real-time scenario, where the computational represents a central issue.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/306162

Multiclass Convolutional Neural Networks for Atrial Fibrillation Classification
Proceeding of the Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Autore/i: Sbrollini, Agnese; Tomassini, Selene; Emaldi, Enrico; Marcantoni, Ilaria; Morettini, Micaela; Dragoni, Aldo F; Burattini, Laura
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Atrial fibrillation (AF) is a common supraventricular arrhythmia. Its automatic identification by standard 12-lead electrocardiography (ECG) is still challenging. Recently, deep learning provided new instruments able to mimic the diagnostic ability of clinicians but only in case of binary classification (AF vs. normal sinus rhythm-NSR). However, binary classification is far from the real scenarios, where AF has to be discriminated also from several other physiological and pathological conditions. The aim of this work is to present a new AF multiclass classifier based on a convolutional neural network (CNN), able to discriminate AF from NSR, premature atrial contraction (PAC) and premature ventricular contraction (PVC). Overall, 2796 12-lead ECG recordings were selected from the open-source "PhysioNet/Computing in Cardiology Challenge 2021" database, to construct a dataset constituted by four balanced classes, namely AF class, PAC class, PVC class, and NSR class. Each lead of each ECG recording was decomposed into spectrogram by continuous wavelet transform and saved as 2D grayscale images, used to feed a 6-layers CNN. Considering the same CNN architecture, a multiclass classifiers (all classes) and three binary classifiers (AF class, PAC class, and PVC class vs. NSR class) were created and validated by a stratified shuffle split cross-validation of 10 splits. Performance was quantified in terms of area under the curve (AUC) of the receiver operating characteristic. Multiclass classifier performance was high (AF class: 96.6%; PAC class: 95.3%; PVC class: 92.8%; NSR class: 97.4%) and preferable to binary classifiers. Thus, our CNN AF multiclass classifier proved to be an efficient tool for AF discrimination from physiological and pathological confounders. Clinical Relevance-Our CNN AF multiclass classifier proved to be suitable for AF discrimination in real scenarios.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/306481

Segmented-Beat Modulation Method-Based Procedure for Extraction of Electrocardiogram-Derived Respiration from Data Acquired by Wearable Sensors During High-Altitude Activity
Computing in Cardiology
Autore/i: Sbrollini, A.; Bondi, D.; Romagnoli, S.; Morettini, M.; Marcantoni, I.; Pietrangelo, T.; Verratti, V.; Burattini, L.
Editore: IEEE Computer Society
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: High-altitude sports are affected by hypoxic stress-related alterations and, consequently, may trigger severe events such as sport-related sudden death; thus, into-the-jield monitoring of respiration is essential. A Segmented-Beat Modulation Method (SBMM)-based procedure was previously proposed to extract electrocardiogram (ECG)- derived respiration (EDR). The aim of this study is to validate SBMM-based procedure for EDR extraction in data acquired by wearable sensors during high-altitude physical activities. Respiration signal (RES) and ECG were recorded using BioHarness 3.0 by Zephyr from 3 expeditioners, while performing a trek up to 4, 556m of altitude. EDR it was extracted from ECG by SBMM-based procedure. RES and EDR were segmented into 60-second windows and characterized in terms of breathing rate (BRRES and BREDR, respectively). BRRES and BREDR were compared by absolute difference (|δ|), concordance correlation coefficient (CCC) and linear regression analysis. Results confirmed EDR goodness, proved by low values of |δ| (2[1;4]cpm), satisfactory CCC(0.62; P-value < 0.05) and good fit of regression line (BRRES=0.91· BREDR+4.47cpm). In conclusion, SBMM-based procedure is a good method to extract EDR from data acquired by wearable sensors during high-altitude physical activities.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/314455

Spectral F-wave index for automatic identification of atrial fibrillation in very short electrocardiograms
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
Autore/i: Sbrollini, A.; Marcantoni, I.; Morettini, M.; Burattini, L.
Classificazione: 1 Contributo su Rivista
Abstract: Micro as well as clinical atrial fibrillation (AF) is associated with both F-wave occurrence and high heart-rate variability (HRV). Automatic AF identification typically relies on HRV evaluation only. However, high HRV is not AF specific and may not be reliably estimated in very short electrocardiograms (ECG). This study presents a new algorithm for automatic AF identification in very short ECG based on computation of a new spectral F-wave index (SFWI). Data consisted of short (9 heartbeats) 12-lead ECG acquired from 6628 subjects divided in assessment dataset and validation dataset. Each lead was independently analyzed so that 12 values of SFWI, indicating the percentage of spectral power in the 4–10 Hz band, were obtained for each ECG. Additionally, a global SFWI value was computed as the median of SFWI distribution over leads. To identify AF, a threshold on SFWI was firstly assessed on the assessment dataset, and then evaluated on the validation dataset by computation of sensitivity (SE), specificity (SP) and accuracy (AC). Results were compared with those of standard HRV-based approaches. AF identification by SFWI was already good when considering a single lead (SE: 84.6%–88.8%, SP: 84.5%–87.0%, AC: 84.5%–87.3%), improved significantly when combining the 12 leads (SE: 89.0%, SP: 87.0%, AC: 88.7%) and, overall, performed better than standard HRV-based approaches (SE: 82.2%, SP: 83.6%, AC: 83.4%). The presented algorithm is a useful tool to automatically identify AF in very short ECG, and thus has the potentiality to be applied for detection of both micro and clinical AF.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/292667

Initial Reference Values of Electrocardiographic Alternans by Enhanced Adaptive Matched Filter
Computing in Cardiology
Autore/i: Marcantoni, I.; Iammarino, E.; Sbrollini, A.; Morettini, M.; Burattini, L.
Editore: IEEE Computer Society
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Electrocardiographic alternans (ECGA) is the ABAB fluctuation of the electrocardiogram (ECG) and may manifest as P-wave/QRS-complex/T-wave alternans (PWA/QRSA/TWA). ECGA is a cardiovascular risk index, and its characterization may depend on the automatic identification method. Normal ranges (needed to define risk conditions) are still not available for the new enhanced adaptive matched filter (EAMF) method. Thus, the present study aims to provide them. EAMF was used to characterize ECGA (in terms of: amplitude, μ V; area, μ V× ms; and duration, number of beats) in 15-lead ECG from 52 healthy subjects (39/13 male/female), from the 'PTB Diagnostic ECG Database'. Median ECGA values over leads and subjects were: 2μ V, 200μ V× ms, and 17 beats for PWA; 1 μ V, 80 μ V× ms, and 8 beats for QRSA; and 7 μ V, 1300μ V× ms, and 49 beats for TWA. ECGA in females (PWA:4 μ V, 350 μ V× ms, and 22 beats; QRSA: 1 μ V, 80 μ V × ms, and 11 beats; TWA: 10 μ V; 2000 μ V× ms, and 49 beats) was higher (∗p < 0.05) than ECGA in males (PWA: 20 μ V∗, 200 μ V× ms∗, and 16 beats∗ QRSA: 1 μ V, 80 μ V× ms, and 7 beats; TWA: 6μ V, 1150 μ V× ms, and 48 beats). Maximum ECGA values were observed in fundamental leads. The observed reference ECGA values seem reliable if comparing with pathological populations but are initial and analysis of wider datasets is needed.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/314451

Unraveling the Factors Determining Development of Type 2 Diabetes in Women With a History of Gestational Diabetes Mellitus Through Machine-Learning Techniques
FRONTIERS IN PHYSIOLOGY
Autore/i: Ilari, L.; Piersanti, A.; Gobl, C.; Burattini, L.; Kautzky-Willer, A.; Tura, A.; Morettini, M.
Classificazione: 1 Contributo su Rivista
Abstract: Gestational diabetes mellitus (GDM) is a type of diabetes that usually resolves at the end of the pregnancy but exposes to a higher risk of developing type 2 diabetes mellitus (T2DM). This study aimed to unravel the factors, among those that quantify specific metabolic processes, which determine progression to T2DM by using machine-learning techniques. Classification of women who did progress to T2DM (labeled as PROG, n = 19) vs. those who did not (labeled as NON-PROG, n = 59) progress to T2DM has been performed by using Orange software through a data analysis procedure on a generated data set including anthropometric data and a total of 34 features, extracted through mathematical modeling/methods procedures. Feature selection has been performed through decision tree algorithm and then Naïve Bayes and penalized (L2) logistic regression were used to evaluate the ability of the selected features to solve the classification problem. Performance has been evaluated in terms of area under the operating receiver characteristics (AUC), classification accuracy (CA), precision, sensitivity, specificity, and F1. Feature selection provided six features, and based on them, classification was performed as follows: AUC of 0.795, 0.831, and 0.884; CA of 0.827, 0.813, and 0.840; precision of 0.830, 0.854, and 0.834; sensitivity of 0.827, 0.813, and 0.840; specificity of 0.700, 0.821, and 0.662; and F1 of 0.828, 0.824, and 0.836 for tree algorithm, Naïve Bayes, and penalized logistic regression, respectively. Fasting glucose, age, and body mass index together with features describing insulin action and secretion may predict the development of T2DM in women with a history of GDM.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/297161

Review on Cardiorespiratory Complications after SARS-CoV-2 Infection in Young Adult Healthy Athletes
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
Autore/i: Romagnoli, S.; Sbrollini, A.; Marcantoni, I.; Morettini, M.; Burattini, L.
Classificazione: 1 Contributo su Rivista
Abstract: This review analyzes scientific data published in the first two years of the COVID-19 pandemic with the aim to report the cardiorespiratory complications observed after SARS-CoV-2 infection in young adult healthy athletes. Fifteen studies were selected using PRISMA guidelines. A total of 4725 athletes (3438 males and 1287 females) practicing 19 sports categories were included in the study. Information about symptoms was released by 4379 (93%) athletes; of them, 1433 (33%) declared to be asymptomatic, whereas the remaining 2946 (67%) reported the occurrence of symptoms with mild (1315; 45%), moderate (821; 28%), severe (1; 0%) and unknown (809; 27%) severity. The most common symptoms were anosmia (33%), ageusia (32%) and headache (30%). Cardiac magnetic resonance identified the largest number of cardiorespiratory abnormalities (15.7%). Among the confirmed inflammations, myocarditis was the most common (0.5%). In conclusion, the low degree of symptom severity and the low rate of cardiac abnormalities suggest that the risk of significant cardiorespiratory involvement after SARS-CoV-2 infection in young adult athletes is likely low; however, the long-term physiologic effects of SARS-CoV-2 infection are not established yet. Extensive cardiorespiratory screening seems excessive in most cases, and classical pre-participation cardiovascular screening may be sufficient.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/300334

Lung nodule diagnosis and cancer histology classification from computed tomography data by convolutional neural networks: A survey
COMPUTERS IN BIOLOGY AND MEDICINE
Autore/i: Tomassini, Selene; Falcionelli, Nicola; Sernani, Paolo; Burattini, Laura; Dragoni, Aldo Franco
Classificazione: 1 Contributo su Rivista
Abstract: Lung cancer is among the deadliest cancers. Besides lung nodule classification and diagnosis, developing non-invasive systems to classify lung cancer histological types/subtypes may help clinicians to make targeted treatment decisions timely, having a positive impact on patients' comfort and survival rate. As convolutional neural networks have proven to be responsible for the significant improvement of the accuracy in lung cancer diagnosis, with this survey we intend to: show the contribution of convolutional neural networks not only in identifying malignant lung nodules but also in classifying lung cancer histological types/subtypes directly from computed tomography data; point out the strengths and weaknesses of slice-based and scan-based approaches employing convolutional neural networks; and highlight the challenges and prospective solutions to successfully apply convolutional neural networks for such classification tasks. To this aim, we conducted a comprehensive analysis of relevant Scopus-indexed studies involved in lung nodule diagnosis and cancer histology classification up to January 2022, dividing the investigation in convolutional neural network-based approaches fed with planar or volumetric computed tomography data. Despite the application of convolutional neural networks in lung nodule diagnosis and cancer histology classification is a valid strategy, some challenges raised, mainly including the lack of publicly-accessible annotated data, together with the lack of reproducibility and clinical interpretability. We believe that this survey will be helpful for future studies involved in lung nodule diagnosis and cancer histology classification prior to lung biopsy by means of convolutional neural networks.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/304482

Editorial: Artificial intelligence in human physiology
FRONTIERS IN PHYSIOLOGY
Autore/i: Ong, Chin Siang; Burattini, Laura; Schena, Stefano
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/309103

Identification and Classification of Driving-Related Stress Using Electrocardiogram and Skin Conductance Signals
Proceeding of 17th IEEE International Symposium on Medical Measurements and Applications
Autore/i: Marcantoni, I.; Barchiesi, G.; Barchiesi, S.; Belbusti, C.; Leoni, C.; Romagnoli, S.; Sbrollini, A.; Morettini, M.; Burattini, L.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: The development of on-board car electronics for automatic stress level detection is becoming an area of great interest. The literature showed that biomedical signal acquisition could provide significant information. Skin conductance (SC) and electrocardiogram (ECG) have demonstrated to provide the most significant stress-related features. Thus, the aim of this study is the classification of three-level and binary stress, using a minimal combination of SC and ECG features. The 'Stress Recognition in Automobile Drivers' database was used to test a procedure based on linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA). The database protocol includes three driving periods, corresponding to different levels of stress (low-medium-high). After data preprocessing, LDA and QDA three-level classifications were applied on all the extracted SC and ECG features to determine the best classification approach. Boruta algorithm allowed to select the most significant features for the classification. Then, the best classification approach was applied on this restricted set of features, performing both three-level (low vs medium vs high) and binary (high+medium vs low) stress classification. QDA was the most accurate classification method (accuracy: 96.0% for QDA vs 85.3% for LDA, considering all the features). QDA accuracy, considering only the selected features, was 86.7% for the three-level classification and 94.7% for the binary classification. This result represents an acceptable trade-off between classification accuracy and computational cost, associated to the number of considered features. In conclusion, ECG together with SC are suitable for the objective and automatic identification and classification of driving-related stress with a good accuracy.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/306161

Neonatal Clinical Outcomes: a Comparative Analysis
Proceeding of 17th IEEE International Symposium on Medical Measurements and Applications
Autore/i: Sbrollini, A.; Romagnoli, S.; Marcantoni, I.; Burattini, L.; Morettini, M.; Burattini, L.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: The most popular neonatal clinical outcomes, which are blood pH (PH), base excess (BE) and Apgar after 5 minutes from birth (AP5), may provide contrasting information. Thus, aim of the paper is to perform a critical evaluation and comparison of PH, BE and AP5. Reliability of neonatal clinical outcomes was evaluated in relation to perinatal features. Neonatal and fetal cardiotocographic data of 391 newborns (CTU-CHB Intrapartum Cardiotocography Database) were analyzed. Newborns were classified as positive (i.e., as showing critical conditions) if PH<7.10 or BE<-10mmol/l or AP5<7, as negative (i.e., as showing healthy conditions) otherwise. Agreement between pairs of neonatal clinical outcomes was evaluated by computing the correlation coefficient. Fetal decelerations were characterized in terms of rate of occurrence, depth, mean, duration, and area. Correlation between PH and BE, PH and AP5 and BE and AP5 was 0.83, 0.45 and 0.38 (P<0.01), respectively; 329 newborns (84%) were equally classified by all neonatal clinical outcomes, 5 as positive and 324 as negative. Deceleration depth and rate of occurrence were comparable among positive/negative classes, while deceleration mean, duration and area were systematically higher in the positive than in the negative classes, also statistically only for PH classification. Positive class by PH counted the highest number of small newborns; large newborns were similarly distributed over all positive classes. Objective neonatal clinical outcomes, and in particular PH, seems to be more reliable than subjective clinical outcomes, and thus should be preferable for describing neonatal health status.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/306163

TyGIS: improved triglyceride-glucose index for the assessment of insulin sensitivity during pregnancy
CARDIOVASCULAR DIABETOLOGY
Autore/i: Salvatori, Benedetta; Linder, Tina; Eppel, Daniel; Morettini, Micaela; Burattini, Laura; Göbl, Christian; Tura, Andrea
Classificazione: 1 Contributo su Rivista
Abstract: The triglyceride-glucose index (TyG) has been proposed as a surrogate marker of insulin resistance, which is a typical trait of pregnancy. However, very few studies analyzed TyG performance as marker of insulin resistance in pregnancy, and they were limited to insulin resistance assessment at fasting rather than in dynamic conditions, i.e., during an oral glucose tolerance test (OGTT), which allows more reliable assessment of the actual insulin sensitivity impairment. Thus, first aim of the study was exploring in pregnancy the relationships between TyG and OGTT-derived insulin sensitivity. In addition, we developed a new version of TyG, for improved performance as marker of insulin resistance in pregnancy.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/307450

Estimation of Tidal Volume during Exercise Stress Test from Wearable-Device Measures of Heart Rate and Breathing Rate
APPLIED SCIENCES
Autore/i: Sbrollini, A; Catena, R; Carbonari, F; Bellini, A; Sacchetti, M; Burattini, L; Morettini, M
Classificazione: 1 Contributo su Rivista
Abstract: Tidal volume (TV), defined as the amount of air that moves in or out of the lungs with each respiratory cycle, is important in evaluating the respiratory function. Although TV can be reliably measured in laboratory settings, this information is hardly obtainable under everyday living conditions. Under such conditions, wearable devices could provide valuable support to monitor vital signs, such as heart rate (HR) and breathing rate (BR). The aim of this study was to develop a model to estimate TV from wearable-device measures of HR and BR during exercise. HR and BR were acquired through the Zephyr Bioharness 3.0 wearable device in nine subjects performing incremental cycling tests. For each subject, TV during exercise was obtained with a metabolic cart (Cosmed). A stepwise regression algorithm was used to create the model using as possible predictors HR, BR, age, and body mass index; the model was then validated using a leave-one-subject-out cross-validation procedure. The performance of the model was evaluated using the explained variance (R-2), obtaining values ranging from 0.65 to 0.72. The proposed model is a valid method for TV estimation with wearable devices and can be considered not subject-specific and not instrumentation-specific.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/304475

Circadian Modulation of Electrocardiographic Alternans in Kidney Failure Patients on Dialysis
Computing in Cardiology
Autore/i: Marcantoni, I.; Leoni, C.; Peroni, C.; Sbrollini, A.; Morettini, M.; Burattini, L.
Editore: IEEE Computer Society
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Circadian rhythm (periodicity: 24 h) can modulate trends of indices based on the electrocardiogram (ECG), as ECG alternans (ABAB fluctuation of ECG waves; ECGA). This work aims to verify if circadian rhythm modulates ECGA in kidney failure patients - to our knowledge, not investigated yet - and to study the effect of dialysis treatment. ECGA was analyzed on 51 long-term (48 h on average) 12-lead ECG from end-stage renal disease (ESRD) patients. Acquisitions included dialysis, night after, and following day and night. Measures of P-wave, QRS-complex and T-wave alternans (PWA, QRSA, and TWA, respectively; μ V were obtained using the enhanced adaptive matched filter method. Results indicate that, in dialysis-free days, ECGA trend was affected by circadian modulation. PWA/QRSA/TWA trends reached their minima during the night and their maxima during the day (lead average, 7/9/16 μ V and 11/16/20 μ V, respectively; p<0.05). Dialysis interrupted ECGA circadian periodicity, reducing daytime PWA/QRSA/TWA (lead average, 8/12/17 μ V. Generally, ECGA values increased from dialysis to 24 h after, by +39%, +31% and +20% for PWA, QRSA, and TWA, respectively. Thus, in our ESRD population, circadian modulation affected ECGA, and dialysis treatment interrupted its periodicity, causing a decrement of ECGA.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/314452

Sport?Sicuro! A Graphical User Interface for Continuous Cardiovascular Monitoring while Playing Sport Based on Heart Rate and Heart-Rate Variability
2022 Computing in Cardiology (CinC)
Autore/i: Romagnoli, S.; Sbrollini, A.; Marcantoni, I.; Morettini, M.; Burattini, L.
Editore: IEEE Computer Society
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Heart rate (HR) and heart-rate variability (HRV) are increasingly used to assess the body response to heavy physical effort and to define cardiovascular risk indices for sport related sudden cardiac death. The complexity of physiological phenomena affecting HR and HRV makes difficult the interpretation of measures provided by commercial wearable technologies for athletes and trainers. Availability of interactive applications for analysis of HR series can optimize continuous cardiovascular self-monitoring while training. This paper proposes Sport?Sicuro!, a graphical user interface that allows automatic computation of prevention and performance indexes from HR series for cardiovascular monitoring while practicing sport. This tool is an interactive instrument to support self-monitoring of athletes as well as the work of sport medicine clinicians. Sport?Sicuro! was developed under MATLAB. Automatic analysis of HR series is based on some unchangeable features definitions provided in literature, and other arbitrary settings, the default values of which can be changed by the user. Eventually, Sport?Sicuro! provides a report file listing all the quantitative results of the HR analysis. Thus, Sport?Sicuro! represents a potentially useful graphical tool for automatic and objective analysis of HR series in sport.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/314454

Microvolt T-wave alternans in early repolarization syndrome associated with ventricular arrhythmias: A case report
ANNALS OF NONINVASIVE ELECTROCARDIOLOGY
Autore/i: Tondas, Alexander Edo; Batubara, Edwin Adhi Darmawan; Sari, Novi Yanti; Marcantoni, Ilaria; Burattini, Laura
Classificazione: 1 Contributo su Rivista
Abstract: Despite early repolarization (ER) syndrome being usually considered benign, its association with severe/malignant ventricular arrhythmias (VA) was also reported. Microvolt T-wave alternans (MTWA) is an electrocardiographic marker for the development of VA, but its role in ER syndrome remains unknown. A 90-second 6-lead electrocardiogram from an ER syndrome patient, acquired with the Kardia recorder, was analyzed by the enhanced adaptive matched filter for MTWA quantification. On average, MTWA was 50 mu V, higher than what was previously observed on healthy subjects using the same method. In our ER syndrome patient, MTWA plays a potential role in VA development in ER syndrome.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/306461

Feature Contributions to ECG-based Heart-Failure Detection: Deep Learning vs. Statistical Analysis
Computing in Cardiology
Autore/i: Sbrollini, A.; Leoni, C.; De Jongh, M. C.; Morettini, M.; Burattini, L.; Swenne, C. A.
Editore: IEEE Computer Society
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Assessing feature contributions to a specific diagnosis is commonly done by statistical analysis. In the context of heart failure (HF) diagnosis from the electrocardiogram (ECG), this work compares feature contributions assessed by deep learning with those obtained by statistical analysis. Data consists of ECG pairs (baseline and follow-up) from patients with a history of myocardial infarction. When the follow-up ECG was made, controls patients had remained stable, while cases patients had developed HF. The 42 features that characterized each ECG served as inputs of a deep-learning neural network (NN) created by our Repeated Structuring & Learning Procedure. Subject-specific feature ranking was obtained from the local-interpretable model-agnostic explanatory algorithm and processed to obtain feature relevances (FR). Additionally, 42 areas under the curve (AUC) by univariate statistical analysis were obtained. FR and AUC were compared by Pearson's correlation coefficient (p). After training, the NN had a 99% classification performance. FR ranged from 0.32 to 4.47; AUC ranged from 23% to 82%. Correlation analysis yielded no significant association between AUC and FR (ρ=0.18, P-value =0.25). Deep-learning and statistical-analysis feature contributions to HF detection were discordant. Further studies will investigate which of the two approaches better reflects clinical interpretation.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/314453

Brain-on-Cloud for automatic diagnosis of Alzheimer's disease from 3D structural magnetic resonance whole-brain scans
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
Autore/i: Tomassini, Selene; Sbrollini, Agnese; Covella, Giacomo; Sernani, Paolo; Falcionelli, Nicola; Müller, Henning; Morettini, Micaela; Burattini, Laura; Dragoni, Aldo Franco
Classificazione: 1 Contributo su Rivista
Abstract: Background and objective: Alzheimer’s disease accounts for approximately 70% of all dementia cases. Cortical and hippocampal atrophy caused by Alzheimer’s disease can be appreciated easily from a T1- weighted structural magnetic resonance scan. Since a timely therapeutic intervention during the initial stages of the syndrome has a positive impact on both disease progression and quality of life of af- fected subjects, Alzheimer’s disease diagnosis is crucial. Thus, this study relies on the development of a robust yet lightweight 3D framework, Brain-on-Cloud, dedicated to efficient learning of Alzheimer’s disease-related features from 3D structural magnetic resonance whole-brain scans by improving our re- cent convolutional long short-term memory-based framework with the integration of a set of data han- dling techniques in addition to the tuning of the model hyper-parameters and the evaluation of its diag- nostic performance on independent test data. Methods: For this objective, four serial experiments were conducted on a scalable GPU cloud service. They were compared and the hyper-parameters of the best experiment were tuned until reaching the best-performing configuration. In parallel, two branches were designed. In the first branch of Brain-on- Cloud, training, validation and testing were performed on OASIS-3. In the second branch, unenhanced data from ADNI-2 were employed as independent test set, and the diagnostic performance of Brain-on- Cloud was evaluated to prove its robustness and generalization capability. The prediction scores were computed for each subject and stratified according to age, sex and mini mental state examination. Results: In its best guise, Brain-on-Cloud is able to discriminate Alzheimer’s disease with an accuracy of 92% and 76%, sensitivity of 94% and 82%, and area under the curve of 96% and 92% on OASIS-3 and independent ADNI-2 test data, respectively. Conclusions: Brain-on-Cloud shows to be a reliable, lightweight and easily-reproducible framework for automatic diagnosis of Alzheimer’s disease from 3D structural magnetic resonance whole-brain scans, performing well without segmenting the brain into its portions. Preserving the brain anatomy, its appli- cation and diagnostic ability can be extended to other cognitive disorders. to other cognitive disorders. Due to its cloud nature, computational lightness and fast execution, it can also be applied in real-time diagnostic scenarios providing prompt clinical decision support
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/308181

Heart and Breathing Rate Measurement Using Low Intrusive Monitoring Systems
Lecture Notes in Bioengineering
Autore/i: Gaiduk, M.; Orcioni, S.; Seepold, R.; Madrid, N. M.; Pierleoni, P.; Gentili, A.; Burattini, L.; Sbrollini, A.; Marcantoni, I.; Morettini, M.; Conti, M.
Editore: Springer Science and Business Media Deutschland GmbH
Classificazione: 2 Contributo in Volume
Abstract: In many cases continuous monitoring of vital signals is required and low intrusiveness is an important requirement. Incorporating monitoring systems in the hospital or home bed could have benefits for patients and caregivers. The objective of this work is the definition of a measurement protocol and the creation of a data set of measurements using commercial and low-cost prototypes devices to estimate heart rate and breathing rate. The experimental data will be used to compare results achieved by the devices and to develop algorithms for feature extraction of vital signals.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/307682

Cloud-YLung for Non-Small Cell Lung Cancer Histology Classification from 3D Computed Tomography Whole-Lung Scans
Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Autore/i: Tomassini, Selene; Falcionelli, Nicola; Sernani, Paolo; Sbrollini, Agnese; Morettini, Micaela; Burattini, Laura; Dragoni, Aldo Franco
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Non-Small Cell Lung Cancer (NSCLC) represents up to 85% of all malignant lung nodules. Adenocarcinoma and squamous cell carcinoma account for 90% of all NSCLC histotypes. The standard diagnostic procedure for NSCLC histotype characterization implies cooperation of 3D Computed Tomography (CT), especially in the form of low-dose CT, and lung biopsy. Since lung biopsy is invasive and challenging (especially for deeply-located lung cancers and for those close to blood vessels or airways), there is the necessity to develop non-invasive procedures for NSCLC histology classification. Thus, this study aims to propose Cloud-YLung for NSCLC histology classification directly from 3D CT whole-lung scans. With this aim, data were selected from the openly-accessible NSCLC-Radiomics dataset and a modular pipeline was designed. Automatic feature extraction and classification were accomplished by means of a Convolutional Long Short-Term Memory (ConvLSTM)-based neural network trained from scratch on a scalable GPU cloud service to ensure a machine-independent reproducibility of the entire framework. Results show that Cloud- YLung performs well in discriminating both NSCLC histotypes, achieving a test accuracy of 75% and AUC of 84%. Cloud-YLung is not only lung nodule segmentation free but also the first that makes use of a ConvLSTM-based neural network to automatically extract high-throughput features from 3D CT whole-lung scans and classify them. Clinical relevance- Cloud-YLung is a promising framework to non-invasively classify NSCLC histotypes. Preserving the lung anatomy, its application could be extended to other pulmonary pathologies using 3D CT whole-lung scans.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/306482

Model-Based Assessment of Hepatic and Extrahepatic Insulin Clearance from Short Insulin-Modified IVGTT in Women with a History of Gestational Diabetes
Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Autore/i: Piersanti, A.; Abdul Rahman, N. H. B.; Gobl, C.; Burattini, L.; Kautzky-Willer, A.; Pacini, G.; Tura, A.; Morettini, M.
Editore: NLM (Medline)
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Insulin clearance is an integral component of insulin metabolism. Yet, little is known about separate contribution of hepatic and extrahepatic insulin clearance in type 2 diabetes and in high-risk populations, such as women who experienced gestational diabetes mellitus (pGDM). A model-based method was recently proposed to assess both contributions from 3-hour insulin-modified intravenous glucose tolerance test (IM-IVGTT); the aim of this study was to assess the reliability of short (1 hour) IM-IVGTT in the application of such model-based method and to evaluate the role of the two contributions in determining insulin clearance in pGDM. A total of 115 pGDM women and 41 who remained healthy during pregnancy (CNT) were analyzed early postpartum and underwent a 3-hour IMIVGTT. Peripheral insulin clearance (CLP), hepatic fractional extraction (FEL) and extrahepatic distribution volume (VP) were estimated by performing a best-fit procedure on insulin IMIVGTT data considering firstly the overall 3-hour duration and then limiting data to 1 hour. Results showed no significant difference in parameter values between the 3-hour and the 1-hour IM-IVGTT. Comparison between pGDM and CNT (1-hour) showed no significant difference in CLp (0.23 [0.29] vs. 0.27 [0.43] L·min-1; p=0.64), FEL (50.2 [15.1] vs. 50.9 [11.7] %; p=0.63) and VP (2.01 [2.99] vs. 2.70 [4.00] L; p=0.92). In conclusion, short IM-IVGTT provides a reliable assessment of hepatic and extrahepatic insulin clearance through such model-based method. Its application to the study of pGDM women showed no alteration in hepatic and extrahepatic contributions with respect to women who had a healthy pregnancy.Clinical Relevance- This study proves the reliability of short (1 hour) IM-IVGTT to assess hepatic and extrahepatic insulin clearance in women who experienced gestational diabetes.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/294845

An integrated lumped-parameter model of the cardiovascular system for the simulation of acute ischemic stroke: description of instantaneous changes in hemodynamics
MATHEMATICAL BIOSCIENCES AND ENGINEERING
Autore/i: Civilla, L.; Sbrollini, A.; Burattini, L.; Morettini, M.
Classificazione: 1 Contributo su Rivista
Abstract: Acute Ischemic Stroke (AIS) is defined as the acute condition of occlusion of a cerebral artery and is often caused by a Hypertensive Condition (HC). Due to its sudden occurrence, AIS is not observable the right moment it occurs, thus information about instantaneous changes in hemodynamics is limited. This study aimed to propose an integrated Lumped Parameter (LP) model of the cardiovascular system to simulate an AIS and describe instantaneous changes in hemodynamics. In the integrated LP model of the cardiovascular system, heart chambers have been modelled with elastance systems with controlled pressure inputs; heart valves have been modelled with static open/closed pressure-controlled valves; eventually, the vasculature has been modelled with resistor-inductor-capacitor (RLC) direct circuits and have been linked to the rest of the system through a series connection. After simulating physiological conditions, HC has been simulated by changing pressure inputs and constant RLC parameters. Then, AIS occurring in arteries of different sizes have been simulated by considering time-dependent RLC parameters due to the elimination from the model of the occluding artery; instantaneous changes in hemodynamics have been evaluated by Systemic Arteriolar Flow (Qa) and Systemic Arteriolar Pressure (Pa) drop with respect to those measured in HC. Occlusion of arteries of different sizes leaded to an average Qa drop of 0.38 ml/s per cardiac cycle (with minimum and maximum values of 0.04 ml/s and 1.93 ml/s) and average Pa drop of 0.39 mmHg, (with minimum and maximum values of 0.04 mmHg and 1.98 mmHg). In conclusion, hemodynamic variations due to AIS are very small with respect to HC. A direct relation between the inverse of the length of the artery in which the occlusion occurs and the hemodynamic variations has been highlighted; this may allow to link the severity of AIS to the length of the interested artery.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/290662

Adaptive bradycardia assessment in preterm infants
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
Autore/i: Sbrollini, A.; Mancinelli, M.; Marcantoni, I.; Morettini, M.; Carnielli, V. P.; Burattini, L.
Classificazione: 1 Contributo su Rivista
Abstract: In preterm infants, bradycardias associate to critical health conditions. Standard algorithm for bradycardia identification assumes baseline heart rate (BHR) equal to 150bpm and identifies bradycardias when heart rate (HR) decreases below 100bpm. Since preterm infants show BHR varying from 120bpm to 160bpm, a new adaptive algorithm for real-time bradycardia identification was presented. The adaptive algorithm continuously adjusts BHR by averaging HR over the preceding 10-minute window after eliminating out-of-range HR values, and identifies bradycardias when HR decreases below 67% of BHR. Both standard and adaptive algorithms were evaluated using long-term (20.3–70.3h) electrocardiographic recordings of ten preterm infants (“Preterm Infant Cardio-respiratory Signals” database by Physionet). Bradycardias were characterized in terms of rate (BR, h−1) and depth (BD, bpm). Being also indexes of infants’ health conditions, gestational age at birth (GA, days), birth weight (BW, kg) and HR were used to evaluate performances of the algorithms. Association between BR and BD vs GA, BW and HR was evaluated by computation of the correlation coefficient (ρ). Overall, standard and adaptive algorithms identified 516 and 546 bradycardias, respectively; median BR and BD values were comparable (1.25h−1 and 76bpm vs 1.26h−1 and 70bpm, respectively). However, the adaptive algorithm provided higher BD for HR>150bpm, and vice versa. Significant (p value<0.05) correlations were found between BR and HR (ρ=0.69), BR and BW (ρ=−0.76), and BR and HR (ρ=0.76) only when using the adaptive algorithm. Thus, the adaptive algorithm is superior to the standard algorithm and represents a potentially clinically useful tool for real-time bradycardia assessment in preterm infants.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/290831

Electrocardiogram-based index for the assessment of drug-induced hERG potassium channel block
JOURNAL OF ELECTROCARDIOLOGY
Autore/i: Burattini, L.; Sbrollini, A.; Scinocca, L.; Peroni, C.; Marcantoni, I.; Morettini, M.
Classificazione: 1 Contributo su Rivista
Abstract: Introduction: Drug-induced block of the hERG potassium channel could predispose to torsade de pointes, depending on occurrence of concomitant blocks of the calcium and/or sodium channels. Since the hERG potassium channel block affects cardiac repolarization, the aim of this study was to propose a new reliable index for non-invasive assessment of drug-induced hERG potassium channel block based on electrocardiographic T-wave features. Methods: ERD30% (early repolarization duration) and TS/A (down-going T-wave slope to T-wave amplitude ratio) features were measured in 22 healthy subjects who received, in different days, doses of dofetilide, ranolazine, verapamil and quinidine (all being hERG potassium channel blockers and the latter three being also blockers of calcium and/or sodium channels) while undergoing continuous electrocardiographic acquisition from which ERD30% and TS/A were evaluated in fifteen time points during the 24 h following drug administration (“ECG Effects of Ranolazine, Dofetilide, Verapamil, and Quinidine in Healthy Subjects” database by Physionet). A total of 1320 pairs of ERD30% and TS/A measurements, divided in training (50%) and testing (50%) datasets, were obtained. Drug-induced hERG potassium channel block was modelled by the regression equation BECG(%) = a·ERD30% + b·TS/A+ c·ERD30%·TS/A + d; BECG(%) values were compared to plasma-based measurements, BREF(%). Results: Regression coefficients values, obtained on the training dataset, were: a = −561.0 s−1, b = −9.7 s, c = 77.2 and d = 138.9. In the testing dataset, correlation coefficient between BECG(%) and BREF(%) was 0.67 (p < 10−81); estimation error was −11.5 ± 16.7%. Conclusion: BECG(%) is a reliable non-invasive index for the assessment of drug-induced hERG potassium channel block, independently from concomitant blocks of other ions.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/293722

Ensemble empirical mode decomposition for efficient r-peak detection in electrocardiograms acquired by portable sensors during sport activity
2021 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2021 - Conference Proceedings
Autore/i: Romagnoli, S.; Marcantoni, I.; Campanella, K.; Sbrollini, A.; Morettini, M.; Burattini, L.
Editore: Institute of Electrical and Electronics Engineers Inc.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Wearable and portable electrocardiographic devices are revolutionizing athlete's screening through digital health application enabling a continuous monitoring of important cardiac parameters in real-time. Automatic examination of electrocardiogram (ECG) acquired during sport activity is challenging because acquisition conditions often lead to record ECGs with low signal to noise ratio (SNR). The initial issue of automatic ECG analysis is the identification of R peaks. R peaks are fundamental for the estimation of heart rate, which is the primary clinical parameter used by athletes for athletic performance evaluation. Thus, the aim of this research is to propose an R-peak detection algorithm for ECGs acquired during sport activity by portable and wearable sensors dealing with low SNR. The algorithm is based on a noise assisted data analysis method: Ensemble Empirical Mode Decomposition method (EEMD). Localization of R peaks is primarily performed on the first intrinsic mode function extracted by the EEMD. The algorithm was tested on 'Run on indoor treadmill' dataset from Physionet. ECGs were acquired during running/light jogging on an indoor treadmill and present a low SNR (1±7 dB). The developed EEMD-based algorithm showed good performances in terms of positive predicted value (91.08%), sensitivity (92.76%), false discovery rate (8.92), false negative rate (7.24%), cumulative statistical index (83.84%) and mean R-peak position error 1.10 [0.46;1.46]ms. EEMD-based algorithm performs efficiently also in computing heart rate. In conclusion, the developed R-peak detection EEMD-based algorithm showed good level of performances even working on low-SNR ECG acquired during sport activity by portable sensors.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/291911

Comparison of software packages for the analysis of continuous glucose monitoring data
2021 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2021 - Conference Proceedings
Autore/i: Piersanti, A.; Giurato, F.; Burattini, L.; Tura, A.; Morettini, M.
Editore: Institute of Electrical and Electronics Engineers Inc.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: The use of Continuous Glucose Monitoring (CGM) systems in the management of diabetes is rapidly growing and represents an eligible technology to overcome the limitations of self-monitoring of blood glucose. However, not complete standardization of the CGM data analyses methodologies is limiting the potential of these devices. In the last few years, different software solutions have been proposed to find a common pattern for making CGM data analysis results more interpretable and reproducible. The aim of this study was to compare two of the newest open-source software packages available for CGM data analysis, GLU and iglu. To perform the comparison, CGM data of 9 subjects with type 1 diabetes coming from the open D1NAMO dataset have been analyzed with both software. Metrics available both in GLU and iglu have been compared, namely: Area Under the Curve (AUC), Time Above Range (TAR), Time Below Range (TBR), Time in Range (TIR) and Mean Absolute Deviation (MAD). Mean values for GLU and iglu were: AUC (170 ± 23 vs. 165 ± 27 mg•dl-1); TAR (40 ± 17 vs. 38 ± 21 %); TBR (6 ± 7 % in both); TIR (54 ± 18 vs. 60 ± 21 %), MAD (43 ± 20 vs. 67 ± 28 mg•dl-1). Only MAD was found statistically different between GLU and iglu. In conclusion, this comparison provided an overview of the graphical and computational aspects in CGM analysis provided by GLU and iglu software packages, which could be useful to researchers and clinicians to find a transparent and consistent way of interpreting CGM data.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/291910

Mathematical Model of Glucagon Kinetics for the Assessment of Insulin-Mediated Glucagon Inhibition During an Oral Glucose Tolerance Test
FRONTIERS IN ENDOCRINOLOGY
Autore/i: Morettini, Micaela; Burattini, Laura; Göbl, Christian; Pacini, Giovanni; Ahrén, Bo; Tura, Andrea
Classificazione: 1 Contributo su Rivista
Abstract: Glucagon is secreted from the pancreatic alpha cells and plays an important role in the maintenance of glucose homeostasis, by interacting with insulin. The plasma glucose levels determine whether glucagon secretion or insulin secretion is activated or inhibited. Despite its relevance, some aspects of glucagon secretion and kinetics remain unclear. To gain insight into this, we aimed to develop a mathematical model of the glucagon kinetics during an oral glucose tolerance test, which is sufficiently simple to be used in the clinical practice. The proposed model included two first-order differential equations -one describing glucagon and the other describing C-peptide in a compartment remote from plasma - and yielded a parameter of possible clinical relevance (i.e., SGLUCA(t), glucagon-inhibition sensitivity to glucose-induced insulin secretion). Model was validated on mean glucagon data derived from the scientific literature, yielding values for SGLUCA(t) ranging from -15.03 to 2.75 (ng of glucagon·nmol of C-peptide-1). A further validation on a total of 100 virtual subjects provided reliable results (mean residuals between -1.5 and 1.5 ng·L-1) and a negative significant linear correlation (r = -0.74, p < 0.0001, 95% CI: -0.82 - -0.64) between SGLUCA(t) and the ratio between the areas under the curve of suprabasal remote C-peptide and glucagon. Model reliability was also proven by the ability to capture different patterns in glucagon kinetics. In conclusion, the proposed model reliably reproduces glucagon kinetics and is characterized by sufficient simplicity to be possibly used in the clinical practice, for the estimation in the single individual of some glucagon-related parameters.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/289574

An innovative training based on robotics for older people with subacute stroke: study protocol for a randomized controlled trial
TRIALS
Autore/i: Maranesi, E.; Bevilacqua, R.; Di Rosa, M.; Pelliccioni, G.; Di Donna, V.; Luzi, R.; Morettini, M.; Sbrollini, A.; Casoni, E.; Rinaldi, N.; Baldoni, R.; Lattanzio, F.; Burattini, L.; Riccardi, G. R.
Classificazione: 1 Contributo su Rivista
Abstract: Background: Stroke is a leading cause of disability, injury, and death in elderly people and represents a major public health problem with substantial medical and economic consequences. The incidence of stroke rapidly increases with age, doubling for each decade after age 55 years. Gait impairment is one of the most important problems after stroke, and improving walking function is often a key component of any rehabilitation program. To achieve this goal, a robotic gait trainer seems to be promising. In fact, some studies underline the efficacy of robotic gait training based on end-effector technology, for different diseases, in particular in stroke patients. In this randomized controlled trial, we verify the efficacy of the robotic treatment in terms of improving the gait and reducing the risk of falling and its long-term effects. Methods: In this single-blind randomized controlled trial, we will include 152 elderly subacute stroke patients divided in two groups to receive a traditional rehabilitation program or a robotic rehabilitation using G-EO system, an end-effector device for the gait rehabilitation, in addition to the traditional therapy. Twenty treatment sessions will be conducted, divided into 3 training sessions per week, for 7 weeks. The control group will perform traditional therapy sessions lasting 50 min. The technological intervention group, using the G-EO system, will carry out 30 min of traditional therapy and 20 min of treatment with a robotic system. The primary outcome of the study is the evaluation of the falling risk. Secondary outcomes are the assessment of the gait improvements and the fear of falling. Further evaluations, such as length and asymmetry of the step, walking and functional status, and acceptance of the technology, will be carried. Discussion: The final goal of the present study is to propose a new approach and an innovative therapeutic plan in the post-stroke rehabilitation, focused on the use of a robotic device, in order to obtain the beneficial effects of this treatment. Trial registration: ClinicalTrials.gov NCT04087083. Registered on September 12, 2019
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/290829

Repeated Structuring & Learning Procedure for Detection of Myocardial Ischemia: a Robustness Analysis
Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Autore/i: Sbrollini, A.; Marcantoni, I.; Morettini, M.; Swenne, C. A.; Burattini, L.
Editore: NLM (Medline)
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Myocardial ischemia, consisting in a reduction of blood flow to the heart, may cause sudden cardiac death by myocardial infarction or trigger serious abnormal rhythms. Thus, its timely identification is crucial. The Repeated Structuring and Learning Procedure (RS&LP), an innovative constructive algorithm able to dynamically create neural networks (NN) alternating structuring and learning phases, was previously found potentially useful for myocardial ischemia detection. However, performance of created NN depends on three parameters, the values of which need to be set a priori by the user: maximal number of layers (NL), maximal number of initializations (NI) and maximal number of confirmations (NC). A robustness analysis of RS&LP to varying values of NL, NI and NC is fundamental for clinical applications concerning myocardial ischemia detection but was never performed before; thus, it was the aim the present study. Thirteen serial ECG features were extracted by pairs of ECGs belonging to 84 cases (patients with induced myocardial ischemia) and 398 controls (patients with no myocardial ischemia) and used as inputs to learn (50% of population) and test (50% of population) NNs with varying values of NL (1,2,3,4,10), NI (50,250,500,1000,1500) and NC (2,5,10,20,50). Performance of obtained NNs was compared in terms of area under the curve (AUC) of the receiver operating characteristics. Overall, 13 NNs were considered; 12 (92%) were characterized by AUC≥80% and 4 (31%) by AUC≥85%. Thus, RS&LP proved to be robust when creating NNs for detecting of myocardial ischemia.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/294844

Initial investigation of athletes’ electrocardiograms acquired by wearable sensors during the pre-exercise phase
THE OPEN BIOMEDICAL ENGINEERING JOURNAL
Autore/i: Romagnoli, S.; Sbrollini, A.; Colaneri, M.; Marcantoni, I.; Morettini, M.; Zitti, G.; Brocchini, M.; Pozzi, M.; Burattini, L.
Classificazione: 1 Contributo su Rivista
Abstract: Aim: The aim of this study is to support large-scale prevention programs fighting sport-related sudden cardiac death by providing a set of electrocardiographic features representing a starting point in the development of normal reference values for the pre-exercise phase. Background: In people with underlying, often unknown, cardiovascular abnormalities, increased cardiovascular load during exercise can trigger sport-related sudden cardiac death. Prevention remains the only weapon to contrast sport-related sudden cardiac death. So far, no reference values have been proposed for electrocardiograms of athletes acquired with wearable sensors in the pre-exercise phase, consisting of the few minutes immediately before the beginning of the training session. Objective: To perform an initial investigation of athletes’ electrocardiograms acquired by wearable sensors during the pre-exercise phase. Methods: The analyzed electrocardiograms, acquired through BioHarness 3.0 by Zephyr, belong to 51 athletes (Sport Database and Cycling Database of the Cardiovascular Bioengineering Lab of the Università Politecnica delle Marche, Italy). Preliminary values consist of interquartile ranges of six electrocardiographic features which are heart rate, heart-rate variability, QRS duration, ST level, QT interval, and corrected QT interval. Results: For athletes 35 years old or younger, preliminary values were [72;91]bpm, [26;47]ms, [85;104]ms, [-0.08;0.08]mm, [326;364]ms and [378;422]ms, respectively. For athletes older than 35 years old, preliminary values were [71;94]bpm, [16;65]ms, [85;100]ms, [-0.11;0.07]mm, [330;368]ms and [394;414]ms, respectively. Conclusion: Availability of preliminary reference values could help identify those athletes who, due to electrocardiographic features out of normal ranges, are more likely to develop cardiac complications that may lead to sport-related sudden cardiac death.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/291630

Enhanced adaptive matched filter for automated identification and measurement of electrocardiographic alternans
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
Autore/i: Marcantoni, I.; Sbrollini, A.; Morettini, M.; Swenne, C. A.; Burattini, L.
Classificazione: 1 Contributo su Rivista
Abstract: Electrocardiographic alternans, consisting of P-wave alternans (PWA), QRS-complex alternans (QRSA) and T-wave alternans (TWA), is an index of cardiac risk. However, only automated TWA measurement methods have been proposed so far. Here, we presented the enhanced adaptive matched filter (EAMF) method and tested its reliability in both simulated and experimental conditions. Our methodological novelty consists in the introduction of a signal enhancement procedure according to which all sections of the electrocardiogram (ECG) but the wave of interest are set to baseline, and in the extraction of the alternans area (AAr) in addition to the standard alternans amplitude (AAm). Simulated data consisted of 27 simulated ECGs representing all combinations of PWA, QRSA and TWA of low (10 μV) and high (100 μV) amplitude. Experimental data consisted of exercise 12-lead ECGs from 266 heart failure patients with an implanted cardioverter defibrillator for primary prevention. EAMF was able to accurately identify and measure all kinds of simulated alternans (absolute maximum error equal to 2%). Moreover, different alternans kinds were simultaneously present in the experimental data and EAMF was able to identify and measure all of them (AAr: 545 μV × ms, 762 μV × ms and 1382 μV × ms; AAm: 5 μV, 9 μV and 7 μV; for PWA, QRSA and TWA, respectively) and to discriminate TWA as the prevalent one (with the highest AAr). EAMF accurately identifies and measures all kinds of electrocardiographic alternans. EAMF may support determination of incremental clinical utility of PWA and QRSA with respect to TWA only.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/289895

A Preliminary Validation of a New Surgical Procedure for the Treatment of Primary Bladder Neck Obstruction Using a Computational Modeling Approach
BIOENGINEERING
Autore/i: Serpilli, Michele; Zitti, Gianluca; Dellabella, Marco; Castellani, Daniele; Maranesi, Elvira; Morettini, Micaela; Lenci, Stefano; Burattini, Laura
Classificazione: 1 Contributo su Rivista
Abstract: A new surgical procedure for the treatment of primary bladder neck obstruction with maintenance of anterograde ejaculation is proposed. In place of monolateral or bilateral bladder neck incision, associated with a loss of ejaculation rate of up to 30%, the new surgical procedure consists of laser drilling the bladder neck with a number of holes and without muscle fiber disrup- tion. The effect of this novel procedure has been studied numerically, with a simplified two-dimen- sional numerical model of the internal urethral sphincter, varying the position and the number of holes in the fibrotic region of the urethral tissue. Results show an improvement of the urethral sphincter opening by increasing the number of holes, ranging from about 6% to 16% of recovery. Moreover, a non-aligned position of holes positively influences the opening recovery. The concen- trations of maximum principal strain and stress have been registered in the proximity of the inter- face between the physiologic and diseased sphincter, and in those regions where the radial thick- ness is significantly thinner. The effects on the first five patients have been included in the study, showing improvement in micturition, lower urinary tract symptoms, sustained ejaculatory func- tion, and quality of life.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/290912

Cardiac Electrical Alternans in Pregnancy: An Observational Study
Computing in Cardiology
Autore/i: Marcantoni, I.; Assogna, R.; Sbrollini, A.; Morettini, M.; Burattini, L.
Editore: IEEE Computer Society
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In pregnancy, if the woman has a cardiovascular disease, her fetus has an increased risk of inherited cardiac genetic disorders. Aim of this study was to evaluate electrocardiographic alternans (ECGA, mu V) of 23 pregnant women, comparing 12 mothers of fetuses with normal rhythm (MumNRF) and 11 mothers of arrhythmic fetuses (MumArrF). ECGA is a noninvasive cardiac electrical risk marker able to reveal heart electrical instability. ECGA manifests in the ECG as P-wave alternans (PWA), QRS alternans (QRSA) and/or T-wave alternans (TWA). Analysis was performed by the enhanced adaptive matched filter method. ECGA distributions were expressed as: median (interquartile range). Comparisons were performed by the Wilcoxon rank-sum test. Although showing similar heart rate (MumNRF: 85 (19) bpm; MumArrF: 90 (13) bpm), ECGA was higher in MumArrF population than MumNRF one (PWA: 9 (7) mu V vs. 14 (14) mu V; QRSA: 9 (10) mu V vs. 17 (16) mu V, TWA: 12 (14) mu Vvs. 28(17) mu V), but only TWA distributions were statistically different. Moreover, TWA was higher than in a female healthy population (on average 18mu V)in 70% of MumArrF, vs. 33% of MumNRF. Thus, higher TWA in our MumArrF seems to reflect a more unstable heart electrical condition of arrhythmic fetuses' mothers than normal-rhythm fetuses' mothers.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/295901

Hepatic and extrahepatic insulin clearance in mice with double deletion of glucagon-like peptide-1 and glucose-dependent insulinotropic polypeptide receptors
BIOMEDICINES
Autore/i: Morettini, M.; Piersanti, A.; Burattini, L.; Pacini, G.; Gobl, C.; Ahren, B.; Tura, A.
Classificazione: 1 Contributo su Rivista
Abstract: The aim of this study was to investigate whether incretins, at physiological levels, affect hepatic and/or extrahepatic insulin clearance. Hepatic and extrahepatic insulin clearance was studied in 31 double incretin receptor knockout (DIRKO) and 45 wild-type (WT) mice, which underwent an Intravenous Glucose Tolerance Test (IVGTT). A novel methodology based on mathematical modeling was designed to provide two sets of values (FEL-P1, CLP-P1; FEL-P2, CLP-P2 ) accounting for hepatic and extrahepatic clearance in the IVGTT first and second phases, respectively, plus the respective total clearances, CLT-P1 and CLT-P2 . A statistically significant difference between DIRKO and WT was found in CLT-P1 (0.61 [0.48–0.82] vs. 0.51 [0.46–0.65] (median [interquartile range]); p = 0.02), which was reflected in the peripheral component, CLP-P1 (0.18 [0.13–0.27] vs. 0.15 [0.11–0.22]; p = 0.04), but not in the hepatic component, FEL-P1 (29.7 [26.7–34.9] vs. 28.9 [25.7–32.0]; p = 0.18). No difference was detected between DIRKO and WT in CLT-P2 (1.38 [1.13–1.75] vs. 1.69 [1.48–1.87]; p = 0.10), neither in CLP-P2 (0.72 [0.64–0.81] vs. 0.79 [0.69–0.87]; p = 0.27) nor in FEL-P2 (37.8 [35.1–43.1] vs. 39.8 [35.8–44.2]; p = 0.46). In conclusion, our findings suggest that the higher insulin clearance observed in DIRKO compared with WT during the IVGTT first phase may be due to its extrahepatic component.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/291634

Real-Time Smart Monitoring System for Atrial Fibrillation Pathology
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
Autore/i: Pierleoni, Paola; Belli, Alberto; Gentili, Andrea; Incipini, Lorenzo; Palma, Lorenzo; Raggiunto, Sara; Sbrollini, Agnese; Burattini, Laura
Classificazione: 1 Contributo su Rivista
Abstract: Atrial Fibrillation (AF) is a common cardiac pathology and, due to its unpredictability, it sometimes remains not detected. Aim of this work is to present a new version of the already published eHealth system, that includes a new real-time Android application for AF detection and monitoring. The proposed eHealth system is composed of a commercial wearable sensor device (Bioharness 3.0 by Zephyr) for cardiac monitoring and a specially developed Android smartphone application. This application is able to real-time processing the raw data sensed from the wearable sensor, providing stress detection, calories consumption estimation, sinus arrhythmia detection, sinus rhythm classification, and apnea detection. As novelty, the new smartphone application also implemented a SVM-based algorithm designed to detect AF episodes by handling electrocardiogram and the heart-rate sequence of the subjects. The performance of the new SVM-based algorithm implemented in eHealth was tested on AF recordings and evaluated in term of sensitivity and specificity. The results show a sensitivity of 78% and a specificity of 66%, making this version of eHealth system suitable for real-time monitoring of AF events.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/272283

Signal Processing for Athletic Cardiovascular Monitoring with Wearable Sensors: Fully Automatic Detection of Training Phases from Heart Rate Data
Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Autore/i: Romagnoli, S.; Sbrollini, A.; Scalese, A.; Marcantoni, I.; Morettini, M.; Burattini, L.
Editore: Institute of Electrical and Electronics Engineers Inc.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Heart rate (HR) recording is a non-invasive, cheap and time-efficient tool for continuous cardiovascular monitoring through wearable technologies in sport applications directly on field. Although, HR measures cannot inform equally on all aspects of cardiac responses to training, given the individual HR kinetic that depends on internal and external influencing factors. Knowledge of the training context is required to correctly compute and interpret HR-derived indices. Training context is characterized by the training phases, their distribution and training load. The aim of this study is to develop an algorithm for automatic detection of training phases in HR series to boost signal processing for athletic cardiovascular monitoring with wearable technologies. The algorithm computes the start and end times of the training phases. It exploits the variance of HR series computed over moving overlapping windows to detect automatically training transition phases. The algorithm was tested on HR series acquired during middle distance running and jogging. The algorithm showed promising results: mean errors were globally lower than 5 s and percentage error did not exceed 5%. Thus, the fully automatic algorithm for detection of training phases can boost HR signal processing for reliable computation and interpretation of HR-derived indices during continuous cardiovascular monitoring with wearable sensors in athletes.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/296401

Innovative technologies and signal processing in perinatal medicine: Volume 1
Innovative Technologies and Signal Processing in Perinatal Medicine: Volume 1
Autore/i: Pani, D.; Rabotti, C.; Signorini, M. G.; Burattini, L.
Editore: Springer International Publishing
Classificazione: 2 Contributo in Volume
Abstract: Pregnancy is a critical time for the health of the mother and the fetus, with important potential risks for both. Tools for antenatal diagnosis and pregnancy monitoring can support prevention and management of potential risks and complications. In particular, the perinatal period, spanning from the third trimester of pregnancy up to one month after birth, is the most critical for the baby. For this reason, in the last decades, biomedical engineering supported and fostered the scientific research towards the identification of new models, parameters, algorithms, and tools that can improve the quality of fetal monitoring, predict the outcomes and allow physicians to intervene in an appropriate manner to ensure a healthy future for the baby. This book follows the First International Summer School on Technologies and Signal Processing in Perinatal Medicine and reflects some of its most important master lectures. It represents a valuable guide for students and young researchers approaching this topic for the first time, as well as experienced researchers and practitioners looking for a clear representation of the themes and techniques presented by recognized experts in the field.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/325878

T-wave alternans identification in direct and indirect fetal electrocardiography
Innovative Technologies and Signal Processing in Perinatal Medicine: Volume 1
Autore/i: Burattini, L.; Marcantoni, I.; Nasim, A.; Burattini, L.; Morettini, M.; Sbrollini, A.
Editore: Springer International Publishing
Classificazione: 2 Contributo in Volume
Abstract: The fetal electrocardiogram (FECG) is the recording of the electrical activity of the fetal heart. Morphologically, FECG shows the standard P-QRS-TU electrocardiographic pattern and a heart rate (HR) of 120-160 bpm. There are two techniques to acquire FECG: the direct one (DI-FECG), with a spiral wire electrode located on the fetal scalp; and the indirect one (IN-FECG), with electrodes located on the mother's abdomen. Fetal T-wave alternans (TWA) represents a possible cause of unexplained fetal deaths; its investigation, however, is challenging. This chapter discusses about this scientific and clinical problem, and proposes a procedure to identify TWA from both DI-FECG and IN-FECG. The procedure includes the following 3 steps: (1) automatic identification of fetal R peaks, performed through the improved fetal Pan-Tompkins Algorithm; (2) FECG filtering, performed through linear filtering and the segmented-beat modulation method; and (3) automatic TWA identification, performed through the heart-rate adaptive match filter. Application of this procedure to 5 DI-FECG and 20 IN-FECG from 5 fetuses confirmed its goodness for fetal applications and suggested that fetuses show TWA even when healthy. Moreover, TWA detected in DI-FECG was comparable to that in IN-FECG, suggesting that TWA identification was reliable also in IN-FECG, which is much more affected by artifacts and interferences than DI-FECG.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/312108

Early temporal prediction of Type 2 Diabetes Risk Condition from a General Practitioner Electronic Health Record: A Multiple Instance Boosting Approach
ARTIFICIAL INTELLIGENCE IN MEDICINE
Autore/i: Bernardini, M.; Morettini, M.; Romeo, L.; Frontoni, E.; Burattini, L.
Classificazione: 1 Contributo su Rivista
Abstract: Early prediction of target patients at high risk of developing Type 2 diabetes (T2D) plays a significant role in preventing the onset of overt disease and its associated comorbidities. Although fundamental in early phases of T2D natural history, insulin resistance is not usually quantified by General Practitioners (GPs). Triglyceride-glucose (TyG) index has been proven useful in clinical studies for quantifying insulin resistance and for the early identification of individuals at T2D risk but still not applied by GPs for diagnostic purposes. The aim of this study is to propose a multiple instance learning boosting algorithm (MIL-Boost) for creating a predictive model capable of early prediction of worsening insulin resistance (low vs high T2D risk) in terms of TyG index. The MIL-Boost is applied to past electronic health record (EHR) patients’ information stored by a single GP. The proposed MIL-Boost algorithm proved to be effective in dealing with this task, by performing better than the other state-of-the-art ML competitors (Recall from 0.70 and up to 0.83). The proposed MIL-based approach is able to extract hidden patterns from past EHR temporal data, even not directly exploiting triglycerides and glucose measurements. The major advantages of our method can be found in its ability to model the temporal evolution of longitudinal EHR data while dealing with small sample size and variability in the observations (e.g., a small variable number of prescriptions for non-hospitalized patients). The proposed algorithm may represent the main core of a clinical decision support system.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/280099

Insulin clearance is altered in women with a history of gestational diabetes progressing to type 2 diabetes
NMCD. NUTRITION METABOLISM AND CARDIOVASCULAR DISEASES
Autore/i: Tura, A.; Gobl, C.; Morettini, M.; Burattini, L.; Kautzky-Willer, A.; Pacini, G.
Classificazione: 1 Contributo su Rivista
Abstract: Background and aims: Insulin clearance is a relevant process in glucose homeostasis. In this observational study, we aimed to assess insulin clearance (ClINS) in women with former gestational diabetes (fGDM) both early after delivery and after a follow-up. Methods and results: We analysed 59 fGDM women, and 16 women not developing GDM (CNT). All women underwent an oral glucose tolerance test (OGTT) yearly, and an insulin-modified intravenous glucose tolerance test (IVGTT) at baseline and at follow-up end (until 7 years). Both IVGTT and OGTT ClINS was assessed as insulin secretion to plasma insulin ratio. We also defined IVGTT first (0–10 min) and second phase (10–180 min) ClINS. We found that 14 fGDM women progressed to type 2 diabetes (PROG), whereas 45 women remained diabetes-free (NONPROG). At baseline, IVGTT ClINS showed alterations in PROG, especially in second phase (0.88 ± 0.10 l·min−1 in PROG, 0.60 ± 0.06 in NONPROG, 0.54 ± 0.07 in CNT, p ≤ 0.03). Differences in ClINS were not found from OGTT. Cox regression analysis showed second phase ClINS as significant type 2 diabetes predictor (hazard ratio = 1.90, 95% confidence interval 1.09–3.30, p = 0.02). Conclusion: This study showed that insulin clearance derived from an insulin-modified IVGTT is notably altered in women with history of GDM progressing towards type 2 diabetes.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/282420

T-Wave Alternans in Nonpathological Preterm Infants
ANNALS OF NONINVASIVE ELECTROCARDIOLOGY
Autore/i: Marcantoni, I.; Sbrollini, A.; Agostinelli, G.; Surace, F. C.; Colaneri, M.; Morettini, M.; Pozzi, M.; Burattini, L.
Classificazione: 1 Contributo su Rivista
Abstract: Background: Sudden infant death syndrome is more frequent in preterm infants (PTI) than term infants and may be due to cardiac repolarization instability, which may manifest as T-wave alternans (TWA) on the electrocardiogram (ECG). Therefore, the aim of the present work was to analyze TWA in nonpathological PTI and to open an issue on its physiological interpretation. Methods: Clinical population consisted of ten nonpathological PTI (gestational age ranging from 293/7 to 342/7 weeks; birth weight ranging from 0.84 to 2.10 kg) from whom ECG recordings were obtained (“Preterm infant cardio-respiratory signals database” by Physionet). TWA was identified through the heart-rate adapting match filter method and characterized in terms of mean amplitude values (TWAA). TWA correlation with several other clinical and ECG features, among which gestational age–birth weight ratio, RR interval, heart-rate variability, and QT interval, was also performed. Results: TWA was variable among infants (TWAA = 26 ± 11 µV). Significant correlations were found between TWAA versus birth weight (ρ = −0.72, p =.02), TWAA versus gestational age–birth weight ratio (ρ = 0.76, p =.02) and TWAA versus heart-rate variability (ρ = −0.71, p =.02). Conclusions: Our preliminary retrospective study suggests that nonpathological PTI show TWA of few tens of µV, the interpretation of which is still an open issue but could indicate a condition of cardiac risk possibly related to the low development status of the infant. Further investigations are needed to solve this issue.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/273413

Model-based assessment of incretin effect from OGTT data in healthy subjects
Convegno Nazionale di Bioingegneria
Autore/i: Morettini, M.; Creato, E.; Di Monte, J.; Ilari, L.; Burattini, L.
Editore: Patron Editore S.r.l.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: The incretin effect is a phenomenon in which insulin response to an Oral Glucose Tolerance Test (OGTT) is higher with respect to the insulin response to a matched isoglycaemic intravenous glucose infusion (I-IGI). The aim of this study was to simplify our previous model describing glucose-insulin regulatory system to allow assessment of the incretin effect in healthy subjects from OGTT glucose and insulin data, without using I-IGI data. The proposed model is characterized by four free parameters and was tested on mean data of two groups of healthy subjects. Free model parameters were estimated with a good precision (CV%<22) and provided values for the incretin effect very similar to the experimental ones (64.2 vs. 63 in the first group of subject and 77.7 vs. 78.1 in the second group). Thus, the proposed model seems to be promising, for the sake of a patient-oriented approach, to assess the incretin effect in healthy subjects only using OGTT glucose and insulin data.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/312349

Artificial neural network for atrial fibrillation identification in portable devices
SENSORS
Autore/i: Marinucci, D.; Sbrollini, A.; Marcantoni, I.; Morettini, M.; Swenne, C. A.; Burattini, L.
Classificazione: 1 Contributo su Rivista
Abstract: Atrial fibrillation (AF) is a common cardiac disorder that can cause severe complications. AF diagnosis is typically based on the electrocardiogram (ECG) evaluation in hospitals or in clinical facilities. The aim of the present work is to propose a new artificial neural network for reliable AF identification in ECGs acquired through portable devices. A supervised fully connected artificial neural network (RSL_ANN), receiving 19 ECG features (11 morphological, 4 on F waves and 4 on heart-rate variability (HRV)) in input and discriminating between AF and non-AF classes in output, was created using the repeated structuring and learning (RSL) procedure. RSL_ANN was created and tested on 8028 (training: 4493; validation: 1125; testing: 2410) annotated ECGs belonging to the “AF Classification from a Short Single Lead ECG Recording” database and acquired with the portable KARDIA device by AliveCor. RSL_ANN performance was evaluated in terms of area under the curve (AUC) and confidence intervals (CIs) of the received operating characteristic. RSL_ANN performance was very good and very similar in training, validation and testing datasets. AUC was 91.1% (CI: 89.1%–93.0%), 90.2% (CI: 86.2%–94.3%) and 90.8% (CI: 88.1%–93.5%) for the training, validation and testing datasets, respectively. Thus, RSL_ANN is a promising tool for reliable identification of AF in ECGs acquired by portable devices.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/282883

Electrocardiographic Alternans in Hemodialysis: A Case Report
Convegno Nazionale di Bioingegneria
Autore/i: Marcantoni, I.; Di Monte, J.; Leoni, C.; Mansour, Z.; Sbrollini, A.; Morettini, M.; Burattini, L.
Editore: Patron Editore S.r.l.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Hemodialysis (HD) is a clinical procedure used to treat patients suffering of chronic kidney failure. Unfortunately, HD patients are at a high risk of sudden cardiac death (SCD). To evaluate SCD risk, it is useful to analyze the electrocardiographic signal (ECG). One of the most popular indexes to investigate SCD is T-wave alternans (TWA), related to the ventricular repolarization segment of the ECG. Electrocardiographic alternans (ECGA), i.e. the prevalent nature of electrical alternans, though, represents a more complete analysis of the cardiac electrical activity, including also the possible presence of alternans on P wave (PWA) and QRS (QRSA), related to atrial depolarization and repolarization phases, respectively. Aim of this study was to obtain a complete evaluation of ECGA on a HD patient during a HD day, by means of the Heart-Rate Adaptive Match filter (HRAMF) method. HRAMF was applied for ECGA analysis on a continuous Holter ECG recording. Considering four macro-time periods PRE-HD, IN-HD, POST-HD and NT-HD (before, during, after HD and during the night, respectively), ECGA was identified as TWA and presented high values (>15µV) in PRE-HD (51 µV) and IN-HD (53 µV), highlighting how these periods are characterized by a higher SCD risk. Two hours after the end of HD, ECGA decreased due to the treatment, able to rebalance electrolytes concentrations. Statistical differences were found between PRE-HD and POST-HD, and PRE-HD and NT-HD (p<10-3). The study suggested a higher cardiac risk (mostly affecting ventricular repolarization) in HD patients; this risk is lower after the end of HD session.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/312348

Model-Based Estimation of Electrocardiographic QT Interval from Phonocardiographic Heart Sounds in Healthy Subjects
Computing in Cardiology
Autore/i: Sbrollini, A.; Morettini, M.; Marcantoni, I.; Burattini, L.
Editore: IEEE Computer Society
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: The electrocardiographic QT interval is an index of cardiac risk commonly used in clinics. Accurate QT measure is challenging, especially in noisy conditions, when acquisitions of phonocardiograms (PCGs) may be more reliable than acquisitions of electrocardiograms (ECGs). However, PCG features are less used in clinics. Thus, aim of the study was to propose a model for indirectly measuring the electrocardiographic QT interval from the phonocardiographic heart sounds in healthy subjects. To this aim, simultaneously acquired PCGs and ECGs of 99 healthy subjects were processed to obtain median PCG and ECG beats. Beat length, S1 onset and S2 onset were identified from the median PCG beat, while QT interval (QT) was measured from the median ECG beat. Then, a regression model was formulated by regression analysis to obtain PCG-based QT estimation (QT) and validated by leave-one-out cross-validation. Correlation coefficient (p) and estimation error were also computed. QT and QT did not differ significantly (model formulation: 362ms vs 358ms; model validation:360ms vs 358ms, respectively; P>0.5) and were significantly correlated (model formulation: p=0.7, p<10-13; model validation: p=0.6, P<10-10); median error is 1 ms (<0.5 in %). Thus, the proposed model provides a reliable estimation of QT interval from PCG heart sounds in healthy subjects.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/288228

COVID-19 in Italy: Dataset of the Italian Civil Protection Department
DATA IN BRIEF
Autore/i: Italian Civil Protection, Department; Morettini, M.; Sbrollini, A.; Marcantoni, I.; Burattini, L.
Classificazione: 1 Contributo su Rivista
Abstract: The database here described contains data of integrated surveillance for the “Coronavirus disease 2019” (abbreviated as COVID-19 by the World Health Organization) in Italy, caused by the novel coronavirus SARS-CoV-2. The database, included in a main folder called COVID-19, has been designed and created by the Italian Civil Protection Department, which currently manages it. The database consists of six folders called ‘aree’ (containing charts of geographical areas interested by containment measures), ‘dati-andamento-nazionale’ (containing data relating to the national trend of SARS-CoV-2 spread), ‘dati-json’ (containing data that summarize the national, provincial and regional trends of SARS-CoV-2 spread), ‘dati-province’ (containing data relating to the provincial trend of SARS-CoV-2 spread), ‘dati-regioni’ (containing data relating to the regional trend of SARS-CoV-2 spread) and ‘schede-riepilogative’ (containing summary sheets relating to the provincial and regional trends of SARS-CoV-2 spread). The Italian Civil Protection Department daily receives data by the Italian Ministry of Health, analyzes them and updates the database. Thus, the database is subject to daily updates and integrations. The database is freely accessible (CC-BY-4.0 license) at https://github.com/pcm-dpc/COVID-19. This database is useful to provide insight on the spread mechanism of SARS-CoV-2, to support organizations in the evaluation of the efficiency of current prevention and control measures, and to support governments in the future prevention decisions.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/277055

Novel recurrence features for prefall and fall detection in backward and forward fall types
Convegno Nazionale di Bioingegneria
Autore/i: Nasim, A.; Nchekwube, D. C.; Khorasani, E.; Van der Maaden, N. E.; Morettini, M.; Burattini, L.
Editore: Patron Editore S.r.l.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Injuries caused by different types of falls are one of the vital health threats to the elder community living independent or otherwise. Characterization and detection of a fall event can trigger an alert and minimize the damage. This work presents recurrence quantification parameters as novel features for characterization of a fall event in case of backward and forward types of falls using data acquired through wearable sensors. Computing cross recurrence plots and recurrence parameters; recurrence rate (RR), determinism (DET) and line entropy (ENT) for pre-fall, fall and post-fall phases, the level of signal stability and non-stability is quantified. The recurrence parameters show a stable behaviour in case of pre-fall (RR=0.74, DET=0.85, ENT=4.36) and chaotic behaviour in case of fall (RR=0.39 DET=0.80, ENT=3.13). To assess the discriminating capability of novel recurrence features, a support vector machine (SVM) is used to perform binary classification for prefall and fall classes. The SVM results in overall accuracy of 76% with a positive prediction of 82% for fall and 70% for pre-fall events. The results indicate that recurrence metrics are successfully able to characterize a sudden fall event and could be used in designing fall detection algorithms using wearable sensors.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/312347

Preface
Innovative Technologies and Signal Processing in Perinatal Medicine: Volume 1
Autore/i: Pani, D.; Rabotti, C.; Signorini, M. G.; Burattini, L.
Editore: Springer International Publishing
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/312109

Extended segmented beat modulation method for cardiac beat classification and electrocardiogram denoising
ELECTRONICS
Autore/i: Nasim, A.; Sbrollini, A.; Morettini, M.; Burattini, L.
Classificazione: 1 Contributo su Rivista
Abstract: Beat classification and denoising are two challenging and fundamental operations when processing digital electrocardiograms (ECG). This paper proposes the extended segmented beat modulation method (ESBMM) as a tool for automatic beat classification and ECG denoising. ESBMM includes four main steps: (1) beat identification and segmentation into PQRS and TU segments; (2) wavelet-based time-frequency feature extraction; (3) convolutional neural network-based classification to discriminate among normal (N), supraventricular (S), and ventricular (V) beats; and (4) a template-based denoising procedure. ESBMM was tested using the MIT–BIH arrhythmia database available at Physionet. Overall, the classification accuracy was 91.5% while the positive predictive values were 92.8%, 95.6%, and 83.6%, for N, S, and V classes, respectively. The signal-to-noise ratio improvement after filtering was between 0.15 dB and 2.66 dB, with a median value equal to 0.99 dB, which is significantly higher than 0 (p < 0.05). Thus, ESBMM proved to be a reliable tool to classify cardiac beats into N, S, and V classes and to denoise ECG tracings.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/283394

AdvFPCG-Delineator: Advanced delineator for fetal phonocardiography
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
Autore/i: Tomassini, S.; Sbrollini, A.; Strazza, A.; Sameni, R.; Marcantoni, I.; Morettini, M.; Burattini, L.
Classificazione: 1 Contributo su Rivista
Abstract: Fetal phonocardiogram (FPCG) consists in the recording of fetal heart sounds by means of a sensor placed on the mother's abdominal surface. Usually, FPCG includes two major sounds for each fetal cardiac cycle: S1, produced by the sudden closure of mitral and tricuspid valves, and S2 produced by the closure of aortic and pulmonary valves. The aim of the present study was to propose AdvFPCG-Delineator for automatic fetal S1 and S2 identification and to demonstrate its reliability in different clinical conditions. The method consists of a wavelet-based filtering procedure followed by the computation of the scalogram, from which S1 and S2 were identified using a threshold-based algorithm. AdvFPCG-Delineator was tested on the “Simulated Fetal PCGs database” (37 FPCG signals) and on the experimental “Shiraz University fetal heart sounds database” (119 FPCG signals), both available at PhysioNet (https://physionet.org). Manual S1 and S2 annotations and simultaneously acquired cardiotocographic recordings were used to compute reference fetal heart rate (FHR) for the simulated and experimental databases, respectively. No statistically significant difference was observed between estimated vs reference FHR (140 bpm vs 140 bpm, respectively) for the simulated database, for which AdvFPCG-Delineator was also able to track beat-to-beat variability (correlation over 92%). Additionally, no statistically significant difference was observed between estimated vs reference FHR (141 bpm vs 140 bpm, respectively) for the experimental database, even when stratifying by clinical conditions (maternal age, gestational age, etc.). In conclusion, AdvFPCG-Delineator proved to be a reliable method to automatically identify S1 and S2 from fetal phonocardiograms.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/282806

Electrocardiographic Alternans in Myocardial Bridge: A Case Report
Computing in Cardiology
Autore/i: Marcantoni, I.; Di Menna, A.; Rossini, F.; Turco, F.; Morettini, M.; Sbrollini, A.; Bianco, F.; Pozzi, M.; Burattini, L.
Editore: IEEE Computer Society
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Myocardial bridge (MB) is a congenital heart condition in which a 'bridge' of myocardium is overlying a 'tunneled' coronary artery. MB can be associated with a series of critical cardiac events. Aim of this study was to evaluate electrocardiographic alternans (ECGA) on a MB patient, being ECGA a cardiac electrical risk index defined as beat-to-beat alternation of electrocardiographic P-wave, QRS-complex and T-wave morphology at stable heart rate. ECGA analysis was performed in a 1-hour 12-lead electrocardiographic recording of a 54 years-old MB male patient at rest by application of the heart-rate adaptive match filter method. Areas of P-wave, QRS and T-wave alternans (PWAA, QRSAA, TWAA) were measured, evaluating also the prevalent among the three. Results showed the prevalent alternans was T-wave alternans, being TWAA on average equal to 6.3 µV×s (PWAA=4.7 µV×s, QRSAA=4.3 µV×s); TWAA prevalence occurrence rate was 94% (PWAA: 5%, QRSAA:1%). TWAA was also found to be significantly correlated (p=0.72, p<10-2) with heart rate. Eventually, TWAA was at least twice higher than in previously analyzed male healthy subjects. Thus, MB seems to be associated to a higher cardiac electrical risk, possibly especially while performing physical activity at high heart rate.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/288230

A functional source separation algorithm to enhance error-related potentials monitoring in noninvasive brain-computer interface
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
Autore/i: Ferracuti, F.; Casadei, V.; Marcantoni, I.; Iarlori, S.; Burattini, L.; Monteriu, A.; Porcaro, C.
Classificazione: 1 Contributo su Rivista
Abstract: Background and objectives: An Error related Potential (ErrP) can be noninvasively and directly measured from the scalp through electroencephalography (EEG), as response, when a person realizes they are making an error during a task (as a consequence of a cognitive error performed from the user). It has been shown that ErrPs can be automatically detected with time-discrete feedback tasks, which are widely applied in the Brain-Computer Interface (BCI) field for error correction or adaptation. In this work, a semi-supervised algorithm, namely the Functional Source Separation (FSS), is proposed to estimate a spatial filter for learning the ErrPs and to enhance the evoked potentials. Methods: EEG data recorded on six subjects were used to evaluate the proposed method based on FFS algorithm in comparison with the xDAWN algorithm. FSS- and xDAWN-based methods were compared also to the Cz and FCz single channel. Single-trial classification was considered to evaluate the performances of the approaches. (Both the approaches were evaluated on single-trial classification of EEGs.) Results: The results presented using the Bayesian Linear Discriminant Analysis (BLDA) classifier, show that FSS (accuracy 0.92, sensitivity 0.95, specificity 0.81, F1-score 0.95) overcomes the other methods (Cz - accuracy 0.72, sensitivity 0.74, specificity 0.63, F1-score 0.74; FCz - accuracy 0.72, sensitivity 0.75, specificity 0.61, F1-score 0.75; xDAWN - accuracy 0.75, sensitivity 0.79, specificity 0.61, F1-score 0.79) in terms of single-trial classification. Conclusions: The proposed FSS-based method increases the single-trial detection accuracy of ErrPs with respect to both single channel (Cz, FCz) and xDAWN spatial filter.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/275324

Postural data from Stargardt's syndrome patients
DATA IN BRIEF
Autore/i: Sbrollini, A.; Agostini, V.; Cavallini, C.; Burattini, L.; Knaflitz, M.
Classificazione: 1 Contributo su Rivista
Abstract: The database is a collection of postural data acquired from 10 patients affected by the rare Stargardt's syndrome, all having the ABCA4 gene mutation, and from 10 control healthy subjects. Specifically, the database includes a file (.xlxs) called SubjectsData and 20 datasets (MATLAB structures) containing postural signals. Each subject performed a total of 15 postural tests, 5 postural tests for 3 different conditions (‘C’: eyes-closed; ‘O’: eyes-open, still target fixation; ‘M’: eyes-open, moving target tracking). For each postural test, 11 postural derived signals (the anterior-posterior force, the medio-lateral force, the vertical force, the plate moment about x axis, the plate moment about y axis, the plate moment about z axis, the plate moment about top plate surface about x axis, the plate moment about top plat surface about y axis, the x-coordinate of the center of pressure, the y-coordinate of the center of pressure, and the free moment about z axis) were computed from 8 raw signals, acquired at the Ophthalmic Hospital of Turin, Italy, through an 8-channel Kistler 9286A force platform connected to a Step32 system. Thus, a total of 285 postural signals (120 raw and 165 derived) are available for each subject. The database may be useful to: (1) investigate postural adaptations of patients affected by Stargardt's syndrome; (2) support definition of rehabilitative procedures to reduce postural instability of patients affected by Stargardt's syndrome; and (3) support investigation on visual control of balance in the general population.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/276464

Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”
DATA IN BRIEF
Autore/i: Romagnoli, S.; Sbrollini, A.; Burattini, L.; Marcantoni, I.; Morettini, M.; Burattini, L.
Classificazione: 1 Contributo su Rivista
Abstract: The proposed dataset provides annotations for the 552 cardiotocographic (CTG) recordings included in the publicly available “CTU-CHB intra-partum CTG database” from Physionet (https://physionet.org/content/ctu-uhb-ctgdb/1.0.0/). Each CTG recording is composed by two simultaneously acquired signals: i) the fetal heart rate (FHR) and ii) the maternal tocogram (representing uterine activity). Annotations consist in the detection of starting and ending points of specific CTG events on both FHR signal and maternal tocogram. Annotated events for the FHR signal are the bradycardia, tachycardia, acceleration and deceleration episodes. Annotated events for the maternal tocogram are the uterine contractions. The dataset also reports classification of each deceleration as early, late, variable or prolonged, in relation to the presence of a uterine contraction. Annotations were obtained by an expert gynecologist with the support of CTG Analyzer, a dedicated software application for automatic analysis of digital CTG recordings. These annotations can be useful in the development, testing and comparison of algorithms for the automatic analysis of digital CTG recordings, which can make CTG interpretation more objective and independent from clinician's experience.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/281117

Compressed Segmented Beat Modulation Method using Discrete Cosine Transform
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Autore/i: Nasim, A.; Sbrollini, A.; Marcantoni, I.; Morettini, M.; Burattini, L.
Editore: Institute of Electrical and Electronics Engineers Inc.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Currently used 24-hour electrocardiogram (ECG) monitors have been shown to skip detecting arrhythmias that may not occur frequently or during standardized ECG test. Hence, online ECG processing and wearable sensing applications have been becoming increasingly popular in the past few years to solve a continuous and long-term ECG monitoring problem. With the increase in the usage of online platforms and wearable devices, there arises a need for increased storage capacity to store and transmit lengthy ECG recordings, offline and over the cloud for continuous monitoring by clinicians. In this work, a discrete cosine transform (DCT) compressed segmented beat modulation method (SBMM) is proposed and its applicability in case of ambulatory ECG monitoring is tested using Massachusetts Institute of Technology-Beth Israel Deaconess Medical Center (MIT-BIH) ECG Compression Test Database containing Holter tape normal sinus rhythm ECG recordings. The method is evaluated using signal-to-noise (SNR) and compression ratio (CR) considering varying levels of signal energy in the reconstructed ECG signal. For denoising, an average SNR of 4.56 dB was achieved representing an average overall decline of 1.68 dBs (37.9%) as compared to the uncompressed signal processing while 95 % of signal energy is intact and quantized at 6 bits for signal storage (CR=2) compared to the original 12 bits, hence resulting in 50% reduction in storage size.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/273186

Fifty Years of Biomedical Engineering: From Origin to Smart Technologies
The First Outstanding 50 Years of "Università Politecnica delle Marche"
Autore/i: Burattini, Laura; Di Nardo, Francesco; Morettini, Micaela; Verdini, Federica; Fioretti, Sandro
Classificazione: 2 Contributo in Volume
Abstract: In Italy, the Bioengineering Community was founded in 1980. The kick-off meeting was held in Montesicuro, a little village near Ancona and organized by Prof. Tommaso Leo from the then-named “Università degli Studi di Ancona” (now Università Politecnica delle Marche, UNIVPM) in cooperation with the nascent National Group of Bioengineering. This chapter aims to produce a brief review of the main results in Biomedical Engineering by UNIVPM during the first 50 years useful to understand the present and to track future contributions for the next 50 years. It is also an occasion to recall the pioneering work on the Bioengineering of the Neuromuscular, Cardiovascular and Metabolic systems performed by our leading colleagues Tommaso Leo, Paolo Mancini and Roberto Burattini, as well as to describe significant research achievements obtained by professors, researchers, post-doc fellows and PhD students who worked and/or are currently working at the UNVPM. Though mainly focusing on research findings in the above cited physiological systems, it is also worth mentioning in this chapter that UNIVPM has also an educational mission, provided by the two Biomedical Engineering courses currently active at the Engineering Faculty: the three-year Bachelor and the two-year Master (in English) courses.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/278101

Electrocardiographic Alternans: A New Approach
IFMBE Proceedings
Autore/i: Marcantoni, I.; Calabrese, D.; Chiriatti, G.; Melchionda, R.; Pambianco, B.; Rafaiani, G.; Scardecchia, E.; Sbrollini, A.; Morettini, M.; Burattini, L.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Alternans is an electrophysiological phenomenon consisting in a beat-to-beat variation of the morphology of an electrocardiographic (ECG) waveform. Literature has particularly studied T-wave alternans (TWA) because it has been widely recognized as a noninvasive and clinically useful index to predict occurrence of malignant ventricular arrhythmias and, eventually, sudden cardiac death. Historically, alternans of other segments of ECG, like P wave (PWA), or QRS complex (QRSA) gained less interest than TWA, but this is an incomplete vision of the action potential (AP). AP is influenced by electrical activity of all myocardial cells, so it is reasonable that all ECG waveforms could be affected by alternans phenomenon. ECG alternans (ECGA) can be intended as the prevalent nature of alternans. This study aimed to use the heart-rate adaptive match filter (AMF) method, previously applied for TWA applications, to detect ECGA. AMF effectiveness was tested on simulated alternating ECG (alternans-amplitude range: 10 µV–200 µV), characterized by single- and multiple-wave alternans (always of the same amplitude and morphology). AMF method proved to be specific, being able to recognize ECGA absence, and particularly sensitive to TWA. In general, in case of singular-wave alternans, AMF correctly identified the type of alternans and correctly determined its amplitude (mean error: 0%). When TWA was combined to PWA or QRSA, only TWA was identified with an overestimation of its amplitude (mean error: 23%). In conclusion, overall AMF proved its effectiveness and specificity in revealing and discriminating ECGA.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/272508

Former gestational diabetes: Mathematical modeling of intravenous glucose tolerance test for the assessment of insulin clearance and its determinants
MATHEMATICAL BIOSCIENCES AND ENGINEERING
Autore/i: Morettini, Micaela; Göbl, Christian; Kautzky-Willer, Alexandra; Pacini, Giovanni; Tura, Andrea; Burattini, Laura
Classificazione: 1 Contributo su Rivista
Abstract: Women with a previous history of gestational diabetes mellitus (GDM) have increased risk of developing GDM in future pregnancies (i.e. recurrent GDM) and also Type 2 Diabetes (T2D). Insulin clearance represents one of the processes regulating glucose tolerance but has been scarcely investigated for its possible impairment in high-risk subjects. The aim of this study was to identify possible determinants of insulin clearance in women with a previous history of GDM. A detailed model-based analysis of a regular 3-hour, insulin-modified intravenous glucose tolerance test (IM-IVGTT) has been performed in women with a previous history of GDM (pGDM, n = 115) and in women who had a healthy pregnancy (CNT, n = 41) to assess total, first-phase and second-phase insulin clearance (ClINS-TOT, ClINS-FP and ClINS-SP) and other metabolic parameters (insulin sensitivity SI, glucose effectiveness SG, beta-cell function and disposition index DI). CLINS-SP was found increased in pGDM with respect to CNT and was found significantly inversely linearly correlated with SG (r = -0.20, p = 0.03, slope: -16.2, 95% CI -30.9 to -1.4, intercept: 1.1, 95% CI 0.7-1.4) and also with DI (r = -0.22, p = 0.02, slope: -10.0, 95% CI -18.5 to -1.6, intercept: 0.9, 95% CI 0.7-1.3). Disposition index, accounting for the combined contribution of insulin sensitivity and beta-cell function, and glucose effectiveness were identified as possible determinants of insulin clearance in women with a previous history of GDM. This may be of relevance for more accurate estimation and prevention of the risk for recurrent GDM and T2D.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/272776

Glucose Effectiveness from Short Insulin-Modified IVGTT and Its Application to the Study of Women with Previous Gestational Diabetes Mellitus
DIABETES & METABOLISM JOURNAL
Autore/i: Morettini, M.; Castriota, C.; Gobl, C.; Kautzky-Willer, A.; Pacini, G.; Burattini, L.; Tura, A.
Classificazione: 1 Contributo su Rivista
Abstract: Background: This study aimed to design a simple surrogate marker (i.e., predictor) of the minimal model glucose effectiveness (SG), namely calculated SG (CSG), from a short insulin-modified intravenous glucose tolerance test (IM-IVGTT), and then to apply it to study women with previous gestational diabetes mellitus (pGDM). Methods: CSG was designed using the stepwise model selection approach on a population of subjects (n=181) ranging from normal tolerance to type 2 diabetes mellitus (T2DM). CSG was then tested on a population of women with pGDM (n=57). Each subject underwent a 3-hour IM-IVGTT; women with pGDM were observed early postpartum and after a follow-up period of up to 7 years and classified as progressors (PROG) or non-progressors (NONPROG) to T2DM. The minimal model analysis provided a reference SG. Results: CSG was described as CSG=1.06×10-2+5.71×10-2×KG/Gpeak, KG being the mean slope (absolute value) of loge glucose in 10-25- A nd 25-50-minute intervals, and Gpeak being the maximum of the glucose curve. Good agreement between CSG and SG in the general population and in the pGDM group, both at baseline and follow-up (even in PROG and NONPROG subgroups), was shown by the Bland-Altman plots (<5% observations outside limits of agreement), and by the test for equivalence (equivalence margin not higher than one standard deviation). At baseline, the PROG subgroup showed significantly lower SG and CSG values compared to the NONPROG subgroup (P<0.03). Conclusion: CSG is a valid SG predictor. In the pGDM group, glucose effectiveness appeared to be impaired in women progressing to T2DM.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/277943

Digital cardiotocography: What is the optimal sampling frequency?
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
Autore/i: Romagnoli, Sofia; Sbrollini, Agnese; Burattini, Luca; Marcantoni, Ilaria; Morettini, Micaela; Burattini, Laura
Classificazione: 1 Contributo su Rivista
Abstract: Cardiotocography (CTG) is the most popular prenatal diagnostic test for establishing fetal health and consists in simultaneous recording of fetal heart rate (FHR, bpm) and maternal uterine contraction (UC, mmHg) traces. Typically, FHR and UC traces are visually analyzed and interpreted by clinicians. Recently, software applications like CTG Analyzer have been developed to support visual CTG interpretation by making it more objective and independent from clinician’s experience. Automatic CTG analysis requires CTG-traces digitalization and thus assessment of a correct sampling frequency (SF). Thus, this paper aims to investigate dependency of automatic CTG analysis on SF in order to identify optimal SF (OSF) for FHR and UC traces that minimizes computational efforts without jeopardizing CTG interpretation. To this aim, the “CTU-CHB intra-partum CTG database” was considered and visually annotated by an expert gynecologist. FHR and UC traces, originally sampled at 4 Hz, were down sampled at 2 Hz, 1 Hz, 0.4 Hz and 0.2 Hz, and automatically analyzed using CTG Analyzer. Eventually, results obtained through automatic analysis were compared to visual annotations, which were taken as reference. A cumulative statistical index (CSI), ranging from 0.00% to 100.00%, was defined as a linear combination of positive-predictive value, sensitivity, false-positive rate and false-negative rate. OSF was defined as the one that maximizes CSI. If CSI was showing the same value for more than one SF, the lowest SF was selected as the optimal since minimizing computational efforts. Results indicate that OSF for FHR is 2 Hz (CSI ≥ 85.41%), whereas OSF for UC is 0.2 Hz (CSI = 75.21%).
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/264306

Insulin clearance in women with a history of gestational diabetes assessed by mathematical model analyses of intravenous glucose tolerance test
IFMBE Proceedings
Autore/i: Morettini, M.; Gobl, C.; Kautzky-Willer, A.; Pacini, G.; Tura, A.; Burattini, L.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Circulating concentrations of insulin are determined by a balance between the secretion rate of insulin from pancreatic beta-cells and insulin degradation (“clearance”). However, limited attention has been devoted to the study of insulin clearance in women with former gestational diabetes mellitus (GDM), which are known to be at increased type 2 diabetes risk. The aim of this study was to provide a detailed analysis of insulin clearance in women with former GDM. A population of 156 white Caucasian women, was analyzed early postpartum (4–6 months after delivery) and classified in two groups: women with previous GDM (pGDM, n = 115) and women that remain healthy during pregnancy (CNT, n = 41). All women underwent a 3-hour Insulin-Modified Intravenous Glucose Tolerance Test (IM-IVGTT). Insulin clearance temporal patterns were derived by mathematical modelling of IM-IVGTT data; average insulin clearance values were also considered during the whole test, and in the first - (0–10 min) and second phase (10–180 min). Insulin clearance temporal patterns were found to be different between CNT and pGDM group (p < 0.0001). Average insulin clearance was found different over the second phase of the test (p = 0.04), being equal to 0.54 [0.41] and 0.59 [0.41] l·min−1 in CNT and pGDM group, respectively. In conclusion, some abnormalities in former GDM women, compared to a group of healthy women were detected. This may be of relevance for more accurate estimation of type 2 diabetes risk.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/272506

Electrocardiogram-Derived Respiratory Signal in Sleep Apnea by Segmented Beat Modulation Method
2019 IEEE 23rd International Symposium on Consumer Technologies, ISCT 2019
Autore/i: Sbrollini, A.; Marcantoni, I.; Nasim, A.; Morettini, M.; Burattini, L.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: The most common sleep disorder is sleep apnea, whose manifestations are long breathing pauses. Sleep apnea assessment is usually performed by polysomnography. During this long-term monitoring, patient respiration and other biosignals are recorded by many sensors, causing a high level of discomfort. Thus, methods able to indirectly estimate the biosignal of interest from the others measured should be preferred. Respiration indirectly measured from electrocardiogram (ECG) is called ECG-derived respiratory (EDR) signal. Recently, Segmented Beat Modulation Method (SBMM) was proposed as a good method for EDR signal estimation in normal breathing. Thus, the aim of this study was to assess the quality of EDR signal estimation by SBMM in pathological events of sleep apnea. With this purpose, sixteen long term polysomnographic recordings from MITBIH Polysomnographic Database were considered. After standard preprocessing, respiration and ECG signals were divided in 30s windows and, in order to match to provided annotations, each window was classified into Normal or Apnea. EDR signal was estimated by SBMM procedure from each ECG window. Respiration and EDR signals were then processed by Fourier analysis to extract respiration frequencies. Respiration frequencies computed from respiration and EDR signals were compared in term of error. Results confirmed the good quality of the estimated EDR signal. Respiration frequency extracted from EDR signal in both Normal (16[13;19]cpm) and Apnea windows (18[15;21]cpm) are equal to those extracted from respiration signal (Normal: 16 [13;19]cpm and Apnea: 18 [15;21]cpm), providing null error distributions. In conclusion, SBMM proved to be a promising tool for EDR signal estimation.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/272432

Bradycardia Assessment in Preterm Infants
IFMBE Proceedings
Autore/i: Sbrollini, A.; Mancinelli, M.; Marcantoni, I.; Morettini, M.; Burattini, L.
Editore: Springer
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Prematurity is a severe condition, usually correlated with critical outcomes. One of the major diseases in preterm infants is bradycardia, defined as the heart rate decreasing under 100 bpm for at least two heartbeats in duration. Usually, bradycardia is considered as a manifestation of immature cardiorespiratory control, but no studies investigated its nature in relation to the different clinical features of preterm infants. Thus, aim of this work is to assess the relation between bradycardia features and the main preterm infant clinical features, weight and gestational age. Ten preterm infants were considered, classified according with three criteria: the weight classification, the gestational age classification and the birth size assessment (that combined the two previous classifications). For each preterm infant, bradycardias are automatically identified and characterized in term of bradycardia features: amplitude, duration and area. Moreover, bradycardia events are classified according with their severity. Finally, bradycardia feature distributions of classes that belong to the same classification criterion were compared. Results seems suggesting that bradycardia features differences are more relevant in preterm infants with different weights than in those with different gestational age, contrary to what expected. Anyway, the best results in term of classification were obtained in the birth size assessment; thus, a combined approach that considers both weight and gestational age is preferable. Moreover, a combined evaluation of amplitude and duration for bradycardia characterization can better assess the severity of this arrhythmia and of the preterm infant clinical status.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/272504

Glucose effectiveness and its components in relation to body mass index
EUROPEAN JOURNAL OF CLINICAL INVESTIGATION
Autore/i: Morettini, M.; Di Nardo, F.; Ingrillini, Laura; Fioretti, S.; Gobl, C.; Kautzky-Willer, A.; Tura, Andrea; Pacini, Giovanni; Burattini, L.
Classificazione: 1 Contributo su Rivista
Abstract: Background: Obesity is known to induce a deterioration of insulin sensitivity (SI), one of the insulin-dependent components of glucose tolerance. However, few studies investigated whether obesity affects also the insulin-independent component, that is glucose effectiveness (SG). This cross-sectional study aimed to analyse SG and its components in different body mass index (BMI) categories. Materials and methods: Three groups of subjects spanning different BMI (kg m−2) categories underwent a 3-h frequently sampled intravenous glucose tolerance test: Lean (LE; 18.5 ≤ BMI < 25, n = 73), Overweight (OW; 25 ≤ BMI < 30, n = 90), and Obese (OB; BMI ≥ 30, n = 41). OB has been further divided into two subgroups, namely Obese I (OB-I; 30 ≤ BMI < 35, n = 27) and Morbidly Obese (OB-M; BMI ≥ 35, n = 14). Minimal model analysis provided SG and its components at zero (GEZI) and at basal (BIE) insulin. Results: Values for SG were 1.98 ± 1.30 × 10−2·min−1 in all subjects grouped and 2.38 ± 1.23, 1.84 ± 0.82, 1.59 ± 0.61 10−2·min−1 in LE, OW and OB, respectively. In all subjects grouped, a significant inverse linear correlation was found between the log-transformed values of SG and BMI (r = −0.3, P < 0.0001). SG was significantly reduced in OW and OB with respect to LE (P < 0.001) but no significant difference was detected between OB and OW (P = 0.35) and between OB-I and OB-M (P = 0.25). Similar results were found for GEZI. BIE was not significantly different among NW, OW and OB (P = 0.11) and between OB-I and OB-M (P ≥ 0.07). Conclusions: SG and its major component GEZI deteriorate in overweight individuals compared to those in the normal BMI range, without further deterioration when BMI increases above 30 kg m−2.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/267129

Classification of drug-induced hERG potassium-channel block from electrocardiographic T-wave features using artificial neural networks
ANNALS OF NONINVASIVE ELECTROCARDIOLOGY
Autore/i: Morettini, M.; Peroni, C.; Sbrollini, A.; Marcantoni, I.; Burattini, L.
Classificazione: 1 Contributo su Rivista
Abstract: Background: Human ether‐à‐go‐go‐related gene (hERG) potassium‐channel block represents a harmful side effect of drug therapy that may cause torsade de pointes (TdP). Analysis of ventricular repolarization through electrocardiographic T‐wave features represents a noninvasive way to accurately evaluate the TdP risk in drug‐safety studies. This study proposes an artificial neural network (ANN) for noninvasive electrocardiography‐ based classification of the hERG potassium‐channel block. Methods: The data were taken from the “ECG Effects of Ranolazine, Dofetilide, Verapamil, and Quinidine in Healthy Subjects” Physionet database; they consisted of median vector magnitude (VM) beats of 22 healthy subjects receiving a single 500 μg dose of dofetilide. Fourteen VM beats were considered for each subject, relative to time‐points ranging from 0.5 hr before to 14.0 hr after dofetilide administration. For each VM, changes in two indexes accounting for the early and the late phases of repolarization, ΔERD30% and ΔTS/A, respectively, were computed as difference between values at each postdose time‐point and the predose time‐point. Thus, the dataset contained 286 ΔERD30%‐ΔTS/A pairs, partitioned into training, validation, and test sets (114, 29, and 143 pairs, respectively) and used as inputs of a two‐layer feedforward ANN with two target classes: high block (HB) and low block (LB). Optimal ANN (OANN) was identified using the training and validation sets and tested on the test set. Results: Test set area under the receiver operating characteristic was 0.91; sensitivity, specificity, accuracy, and precision were 0.93, 0.83, 0.92, and 0.96, respectively. Conclusion: OANN represents a reliable tool for noninvasive assessment of the hERG potassium‐channel block.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/271789

Dofetilide-Induced Microvolt T-Wave Alternans
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Autore/i: Marcantoni, I.; Laratta, R.; Mascia, G.; Ricciardi, L.; Sbrollini, A.; Nasim, A.; Morettini, M.; Burattini, L.
Editore: Institute of Electrical and Electronics Engineers Inc.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Dofetilide is an antiarrhythmic drug that selectively inhibits the rapid component of the delayed rectifier potassium current. The administration of dofetilide may cause ventricular arrhythmias and torsade de pointes. Electrocardiographic (ECG) microvolt T-wave alternans (TWA), an electrophysiologic phenomenon consisting in the beat-to-beat alternation of the T-wave amplitude requiring computerized algorithms to be detected, has also been associated to malignant ventricular arrhythmias. Aim of the present study was to evaluate if dofetilide induces TWA during the 24 hours following administration. The study population consisted of 22 healthy subjects ("ECG Effects of Ranolazine, Dofetilide, Verapamil, and Quinidine in Healthy Subjects" database by Physionet) to whom a 500 μg-dose of dofetilide was administered. For each subject, 10 s ECG were acquired at baseline (0.5 hour before dofetilide administration) and at 15 time points during the 24 hours following the drug administration. ECG were then processed for automatic TWA detection by correlation method. In 21 subjects out of 22, after dofetilide administration, TWA significantly increased to a peak value (median TWA values went from 6 μV at baseline to a max 32 μV; p<0.05), on average after 5 hours, to then come back to values closer to baseline. Thus, in healthy subjects, dofetilide increases occurrence and levels (6 times baseline value on average) of TWA in the hours following its administration.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/273187