336 pubblicazioni classificate nel seguente modo:

Assessing hypoglycemia risk during hemodialysis using an explainable machine learning approach based on continuous glucose monitoring metrics
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
Autore/i: Piersanti, A.; Morettini, M.; Cristino, S.; Giudice, L. L. D.; Burattini, L.; Mosconi, G.; Gobl, C. S.; Mambelli, E.; Tura, A.
Classificazione: 1 Contributo su Rivista
Abstract: Continuous glucose monitoring (CGM) can identify hypoglycemia in hemodialysis (HD) patients, who are at risk for this event. On the other hand, machine learning has remarkable value in CGM-based studies, but none of previous studies addressed the problem of hypoglycemia prediction in HD patients. Therefore, we conducted this study to setup different machine learning models based on CGM data (specifically, CGM metrics) to assess the risk of mild and severe hypoglycemia during HD sessions. We studied a cohort of twenty patients (11 with and 9 without diabetes) undergoing chronic HD. All patients underwent CGM for up to 2 weeks. We identified 92 HD sessions and related pre-HD sessions of 8 h length. HD sessions were used to identify mild (<70 mg/dL) and severe (<54 mg/dL) hypoglycemia, whereas pre-HD sessions were used to compute 48 CGM metrics. We then performed feature selection to identify the most relevant metrics for hypoglycemia prediction. The metrics performance was assessed with binary decision tree, k-nearest neighbors, penalized logistic regression, Naïve Bayes, random forest ensemble algorithm. We found that mild hypoglycemia was best predicted by six metrics (M-value100, TIR70-180, ADRR, MAGE-, MAG30, CONGA1h), whereas severe hypoglycemia by three metrics (TIR70-180, ADRR, CONGA1h). The best overall performance was achieved by the tree, showing area under receiver operating characteristic curve (AUC) equal to 68.2 % for prediction of mild hyperglycemia, and AUC equal to 81.2 % for severe hyperglycemia. Notably, individual hypoglycemia risk assessment has potential to guide personalized HD-related clinical decisions to minimize such risk.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/338314

Role of pre-exercise CGM metrics on hypoglycemic events in young patients with type 1 diabetes
IL DIABETE
Autore/i: Piersanti, Agnese; Del Giudice, Libera Lucia; Göbl, Christian; Burattini, Laura; Tura, Andrea; Morettini, Micaela
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/337440

Exercise effect on insulin sensitivity: model-based and data-driven quantification from OGTT
IL DIABETE
Autore/i: Del Giudice, Libera Lucia; Piersanti, Agnese; Burattini, Laura; Tura, Andrea; Morettini, Micaela
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/337441

Computerized otoscopy image-based artificial intelligence model utilizing deep features provided by vision transformer, grid search optimization, and support vector machine for otitis media diagnosis
NEURAL COMPUTING & APPLICATIONS
Autore/i: Cömert, Zafer; Sbrollini, Agnese; Demircan, Furkancan; Burattini, Laura
Classificazione: 1 Contributo su Rivista
Abstract: Otitis media (OM) is an inflammation of the middle ear, often associated with fluid accumulation and characterized by symptoms such as ear pain, fever, and impaired hearing. Timely and accurate diagnosis of OM is essential to facilitate prompt treatment and mitigate the risk of complications such as hearing loss or chronic infection, particularly in regions with limited access to healthcare professionals. In this study, we introduce an advanced computational model for automated OM diagnosis, utilizing the vision transformer (ViT) architecture to extract highly discriminative features from otoscope images. The proposed approach employs a grid search optimization algorithm in combination with a support vector machine (SVM) classifier to accurately recognize different types of OM based on deep feature representations. All experiments were conducted using a publicly accessible Ear Imagery dataset containing 880 otoscope images, categorized into four distinct classes. As a result, the proposed model demonstrated remarkable efficacy, achieving an impressive accuracy rate of 99.37%. It successfully classified all OM types. At its core, the emergence of advanced computational models in healthcare represents a transformative leap that promises to close gaps in access to medical expertise and revolutionize diagnostic practices. Harnessing the power of machine learning and leveraging vast datasets, these models offer unprecedented accuracy and efficiency, paving the way for early intervention and improving patient outcomes on a global scale.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/338832

Diabetic Retinopathy Detection: A Machine-Learning Approach Based on Continuous Glucose Monitoring Metrics
Advances in Digital Health and Medical Bioengineering
Autore/i: Piersanti, A; Salvatori, B; D'Avino, P; Burattini, L; Göbl, C; Tura, A; Morettini, M
Editore: SPRINGER INTERNATIONAL PUBLISHING AG
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Diabetic Retinopathy (DR) is an extremely common complication of diabetes mellitus (DM) and a timely treatment may decelerate its progression before the occurrence of irreversible vision loss. Machine learning (ML) represents a powerful tool for addressing the massive screening burden, nowadays performed with the time consuming and operator dependent analysis of fundus photography. Continuous glucose monitoring (CGM) are wearable devices whose information could be exploited also in real-time. This study aimed to explore the potential of CGM and ML for DR detection. A classification task was pursued to identify DR class (n = 50) from the non-DR class (NDR, n = 28) based on data from anthropometric characteristics and extracted CGM metrics. Among the tested models, Logistic Regression achieved the best performances (72.7% of classification accuracy), with a balanced number of misclassifications accounting for less than 30% of misclassified cases. The approach could be suitable for real-time applications.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/337436

Enhanced Gait Phases Recognition by EMG and Kinematics Information Fusion and a Minimal Recording Setup
AL-KHWARIZMI ENGINEERING JOURNAL
Autore/i: Mobarak, Rami; Mengarelli, Alessandro; Verdini, Federica; Fioretti, Sandro; Burattini, Laura; Tigrini, Andrea
Classificazione: 1 Contributo su Rivista
Abstract: The limited mobility of lower limb amputees highlights the need for advancements in prosthetic control strategies to restore natural locomotion. This paper proposes an information fusion approach for gait phase recognition using surface electromyography (sEMG) and kinematics data. Time-domain (TD) features were extracted from the myoelectric data and three data-driven models, specifically Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Artificial Neural Network (ANN), were compared in three different input conditions i.e. sEMG features, hip angle, and their fusion. Gait phase estimation results averaged from 40 healthy participants during normal walking with 10 strides per each demonstrated that the proposed fusion approach has consistently outperformed (p<0.0001) the other two conditions achieving a maximum accuracy of 85.48% with SVM. The findings suggest promising applications in prosthetic motion control and rehabilitative exoskeletons, highlighting the potential for improved user-driven strategies in lower limb prostheses.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/331239

85P NSCLC-Pro ClustAI: A machine learning model to prognostically stratify patients with advanced NSCLC treated with immune checkpoint inhibitors
ESMO OPEN
Autore/i: Cognigni, V.; Pecci, F.; Bruschi, G.; Sbrollini, A.; Paoloni, F.; Galassi, T.; Cantini, L.; Santamaria, L.; Gualtieri, M.; Lunerti, V.; Villani, S.; Savino, F. D.; Ambrosini, E.; Chiodi, N.; Agostinelli, V.; Mentrasti, G.; Burattini, L.; Berardi, R.
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/331271

Phasor-Based Myoelectric Synergy Features: A Fast Hand-Crafted Feature Extraction Scheme for Boosting Performance in Gait Phase Recognition
SENSORS
Autore/i: Tigrini, A.; Mobarak, R.; Mengarelli, A.; Khushaba, R. N.; Al-Timemy, A. H.; Verdini, F.; Gambi, E.; Fioretti, S.; Burattini, L.
Classificazione: 1 Contributo su Rivista
Abstract: Gait phase recognition systems based on surface electromyographic signals (EMGs) are crucial for developing advanced myoelectric control schemes that enhance the interaction between humans and lower limb assistive devices. However, machine learning models used in this context, such as Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM), typically experience performance degradation when modeling the gait cycle with more than just stance and swing phases. This study introduces a generalized phasor-based feature extraction approach (PHASOR) that captures spatial myoelectric features to improve the performance of LDA and SVM in gait phase recognition. A publicly available dataset of 40 subjects was used to evaluate PHASOR against state-of-the-art feature sets in a five-phase gait recognition problem. Additionally, fully data-driven deep learning architectures, such as Rocket and Mini-Rocket, were included for comparison. The separability index (SI) and mean semi-principal axis (MSA) analyses showed mean SI and MSA metrics of 7.7 and 0.5, respectively, indicating the proposed approach’s ability to effectively decode gait phases through EMG activity. The SVM classifier demonstrated the highest accuracy of 82% using a five-fold leave-one-trial-out testing approach, outperforming Rocket and Mini-Rocket. This study confirms that in gait phase recognition based on EMG signals, novel and efficient muscle synergy information feature extraction schemes, such as PHASOR, can compete with deep learning approaches that require greater processing time for feature extraction and classification.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/334932

Individual Estimation of Parameters Describing the Glucose-Insulin Regulatory System: A Modeling Approach with Regularization for Reduced-Sampling Oral Glucose Tolerance Test Data
Advances in Digital Health and Medical Bioengineering
Autore/i: Del Giudice, Ll; Piersanti, A; Burattini, L; Tura, A; Morettini, M
Editore: SPRINGER INTERNATIONAL PUBLISHING AG
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: A recent modeling approach of the glucose-insulin regulatory system exploits a regularization procedure to allow estimation of model parameters from a 5-samples oral glucose tolerance test (OGTT). However, the complexity of the regularization procedure may limit its applicability; in addition, a considerable number of OGTT presents only 4 time samples rather than 5. Thus, leveraging such a glucose-insulin system model, the objective of the present work was to simplify the regularization procedure and to adapt the model approach to the case of 4-samples OGTT. Simplifying regularization involved minimizing the sum of squared residuals, in conjunction with applying appropriate weights (w1, w2) to the second derivatives of glucose and insulin vectors. Validation of the approach was conducted using both 5-samples and 4-samples OGTTs. In the case of the 4-sample OGTT, the available data did not provide sufficient information to estimate all 13 parameters, thus the number of parameters was reduced to 9. For the remaining four parameters, two sets of values were derived from 10 and 11-samples OGTTs. The selection between the two options was based on which one provided estimates closer to physiological values. Ultimately, the validation of the suggested approach was carried out for protocols with 4 and 5-samples OGTTs, employing appropriate weights, i.e., w1 = 2 and w2 = 0.0100, and w1 = 0.0680 and w2 = 0.0015, respectively. The obtained results yielded a root mean square error of 1.0258 and 1.3062, respectively.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/337435

Acquisition Devices for Fetal Phonocardiography: A Scoping Review
BIOENGINEERING
Autore/i: Giordano, N.; Sbrollini, A.; Morettini, M.; Rosati, S.; Balestra, G.; Gambi, E.; Knaflitz, M.; Burattini, L.
Classificazione: 1 Contributo su Rivista
Abstract: Timely and reliable fetal monitoring is crucial to prevent adverse events during pregnancy and delivery. Fetal phonocardiography, i.e., the recording of fetal heart sounds, is emerging as a novel possibility to monitor fetal health status. Indeed, due to its passive nature and its noninvasiveness, the technique is suitable for long-term monitoring and for telemonitoring applications. Despite the high share of literature focusing on signal processing, no previous work has reviewed the technological hardware solutions devoted to the recording of fetal heart sounds. Thus, the aim of this scoping review is to collect information regarding the acquisition devices for fetal phonocardiography (FPCG), focusing on technical specifications and clinical use. Overall, PRISMA-guidelines-based analysis selected 57 studies that described 26 research prototypes and eight commercial devices for FPCG acquisition. Results of our review study reveal that no commercial devices were designed for fetal-specific purposes, that the latest advances involve the use of multiple microphones and sensors, and that no quantitative validation was usually performed. By highlighting the past and future trends and the most relevant innovations from both a technical and clinical perspective, this review will represent a useful reference for the evaluation of different acquisition devices and for the development of new FPCG-based systems for fetal monitoring.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/330095

NSCLC-Pro ClustAI: A Machine Learning Model to prognostically stratify patients with advanced NSCLC treated with immune checkpoint inhibitors
ESMO OPEN
Autore/i: Cognigni, V.; Pecci, F.; Bruschi, G.; Sbrollini, A.; Paoloni, F.; Galassi, T.; Cantini, L.; Santamaria, L.; Gualtieri, M.; Lunerti, V.; Villani, S.; Savino, F. D.; Ambrosini, E.; Chiodi, N.; Agostinelli, V.; Mentrasti, G.; Burattini, L.; Berardi, R.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/331354

Interhemispheric functional connectivity: an fMRI study in callosotomized patients
FRONTIERS IN HUMAN NEUROSCIENCE
Autore/i: Marcantoni, Ilaria; Piccolantonio, Giusi; Ghoushi, Mojgan; Valenti, Marco; Reversi, Luca; Mariotti, Francesco; Foschi, Nicoletta; Lattanzi, Simona; Burattini, Laura; Fabri, Mara; Polonara, Gabriele
Classificazione: 1 Contributo su Rivista
Abstract: Introduction: Functional connectivity (FC) is defined in terms of temporal correlations between physiological signals, which mainly depend upon structural (axonal) connectivity; it is commonly studied using functional magnetic resonance imaging (fMRI). Interhemispheric FC appears mostly supported by the corpus callosum (CC), although several studies investigating this aspect have not provided conclusive evidence. In this context, patients in whom the CC was resected for therapeutic reasons (split-brain patients) provide a unique opportunity for research into this issue. The present study was aimed at investigating with resting-state fMRI the interhemispheric FC in six epileptic patients who have undergone surgical resection of the CC. Methods: The analysis was performed using fMRI of the Brain Software Library; the evaluation of interhemispheric FC and the recognition of the resting-state networks (RSNs) were performed using probabilistic independent component analysis. Results: Generally, bilateral brain activation was often observed in primary sensory RSNs, while in the associative areas, such as those composing the default mode and fronto-parietal networks, the activation was often unilateral. Discussion: These results suggest that even in the absence of the CC, some interhemispheric communication is still present. This residual FC might be supported through extra-callosal pathways that are likely subcortical, making it possible for some interhemispheric integration. Further studies are needed to confirm these conclusions.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/329836

Quantification of the Individual Effect of an Exercise Bout on Insulin Sensitivity: In-Silico Modeling and Linear Regression Combined to Reduce Sampling Protocol Requirements
2024 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2024 - Proceedings
Autore/i: Del Giudice, L. L.; Piersanti, A.; Burattini, L.; Tura, A.; Morettini, M.
Editore: Institute of Electrical and Electronics Engineers Inc.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Exercise modulates metabolism by also acting on insulin sensitivity, being the biological response to insulin stimulation of target tissues. Among the available methods for insulin sensitivity quantification, an option is represented by mathematical-model interpretation of data from dynamic tests (i.e. administration of a glucose challenge). When using such methods, the requirements in terms of data samples in relation to the model characteristics are critical. Thus, starting from a modeling approach previously proposed in the literature to interpret data from oral glucose tolerance test (OGTT), the aim of this study was to provide a simplified procedure for a reliable (model-based) quantification of the effect of an exercise bout on insulin sensitivity without the need to repeat the full test, as the modelling approach otherwise would require. A mathematical model comprising five differential equations was exploited to estimate 13 unknown parameters, with a particular focus on the parameter kxgi, representing insulin sensitivity. The parameter kxgi and other indexes of insulin sensitivity (Matsuda, Cederholm, Stumvoll, OGIS120 and PREDIM) were assessed in a group of ten men who underwent a 75-gram OGTT before and after an exercise bout. Then, Pearson correlation coefficients were calculated between kxgi percentage changes before and after exercise and change in each of the other indexes, revealing Stumvoll as the most strongly correlated (ρ=0.83). Consequently, a linear regression model was set up to estimate kxgi after exercise from changes in the Stumvoll index (β0=-7.1062, β1=14.6745 for coefficients of the linear regression), thus eliminating the need of two full-protocol OGTT tests.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/334032

Normalization of Electrocardiogram-Derived Cardiac Risk Indices: A Scoping Review of the Open-Access Literature
APPLIED SCIENCES
Autore/i: Iammarino, Erica; Marcantoni, Ilaria; Sbrollini, Agnese; Morettini, Micaela; Burattini, Laura
Classificazione: 1 Contributo su Rivista
Abstract: Featured Application: This scoping review provides an overall assessment of the application of normalization on electrocardiogram-derived cardiac risk indices, aiming to clarify the rationale behind its application and the consistency and comparability of these normalized indices in the literature. Changes in cardiac function and morphology are reflected in variations in the electrocardiogram (ECG) and, in turn, in the cardiac risk indices derived from it. These variations have led to the introduction of normalization as a step to compensate for possible biasing factors responsible for inter- and intra-subject differences, which can affect the accuracy of ECG-derived risk indices in assessing cardiac risk. The aim of this work is to perform a scoping review to provide a comprehensive collection of open-access published research that examines normalized ECG-derived parameters used as markers of cardiac anomalies or instabilities. The literature search was conducted from February to July 2024 in the major global electronic bibliographic repositories. Overall, 39 studies were selected. Results suggest extensive use of normalization on heart rate variability-related indices (49% of included studies), QT-related indices (18% of included studies), and T-wave alternans (5% of included studies), underscoring their recognized importance and suggesting that normalization may enhance their role as clinically useful risk markers. However, the primary objective of the included studies was not to evaluate the effect of normalization itself; thus, further research is needed to definitively assess the impact and advantages of normalization across various ECG-derived parameters.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/336938

Myoelectric-Based Estimation of Vertical Ground Reaction Force During Unconstrained Walking by a Stacked One-Dimensional Convolutional Long Short-Term Memory Model
SENSORS
Autore/i: Mengarelli, A.; Tigrini, A.; Scattolini, M.; Mobarak, R.; Burattini, L.; Fioretti, S.; Verdini, F.
Classificazione: 1 Contributo su Rivista
Abstract: The volitional control of powered assistive devices is commonly performed by mapping the electromyographic (EMG) activity of the lower limb to joints’ angular kinematics, which are then used as the input for regulation. However, during walking, the ground reaction force (GRF) plays a central role in the modulation of the gait, providing dynamic stability and propulsion during the stance phase. Including this information within the control loop of prosthetic devices can improve the quality of the final output, providing more physiological walking dynamics that enhances the usability and patient comfort. In this work, we explored the feasibility of the estimation of the ground reaction force vertical component (VGRF) by using only the EMG activities of the thigh and shank muscles. We compared two deep learning models in three experiments that involved different muscular configurations. Overall, the outcomes show that the EMG signals could be leveraged to obtain a reliable estimation of the VGRF during walking, and the shank muscles alone represent a viable solution if a reduced recording setup is needed. On the other hand, the thigh muscles failed in providing performance enhancements, either when used alone or together with the shank muscles. The results outline the feasibility of including GRF information within an EMG-driven control scheme for prosthetic and assistive devices.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/338317

Identification of Electrocardiographic Patterns Related to Mortality with COVID-19
APPLIED SCIENCES
Autore/i: Sbrollini, Agnese; Leoni, Chiara; Morettini, Micaela; Rivolta, Massimo W.; Swenne, Cees A.; Mainardi, Luca; Burattini, Laura; Sassi, Roberto
Classificazione: 1 Contributo su Rivista
Abstract: COVID-19 is an infectious disease that has greatly affected worldwide healthcare systems, due to the high number of cases and deaths. As COVID-19 patients may develop cardiac comorbidities that can be potentially fatal, electrocardiographic monitoring can be crucial. This work aims to identify electrocardiographic and vectorcardiographic patterns that may be related to mortality in COVID-19, with the application of the Advanced Repeated Structuring and Learning Procedure (AdvRS&LP). The procedure was applied to data from the "automatic computation of cardiovascular arrhythmic risk from electrocardiographic data of COVID-19 patients" (COVIDSQUARED) project to obtain neural networks (NNs) that, through 254 electrocardiographic and vectorcardiographic features, could discriminate between COVID-19 survivors and deaths. The NNs were validated by a five-fold cross-validation procedure and assessed in terms of the area under the curve (AUC) of the receiver operating characteristic. The features' contribution to the classification was evaluated through the Local-Interpretable Model-Agnostic Explanations (LIME) algorithm. The obtained NNs properly discriminated between COVID-19 survivors and deaths (AUC = 84.31 +/- 2.58% on hold-out testing datasets); the classification was mainly affected by the electrocardiographic-interval-related features, thus suggesting that changes in the duration of cardiac electrical activity might be related to mortality in COVID-19 cases.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/334213

Intelligent Human–Computer Interaction: Combined Wrist and Forearm Myoelectric Signals for Handwriting Recognition
BIOENGINEERING
Autore/i: Tigrini, A.; Ranaldi, S.; Verdini, F.; Mobarak, R.; Scattolini, M.; Conforto, S.; Schmid, M.; Burattini, L.; Gambi, E.; Fioretti, S.; Mengarelli, A.
Classificazione: 1 Contributo su Rivista
Abstract: Recent studies have highlighted the possibility of using surface electromyographic (EMG) signals to develop human–computer interfaces that are also able to recognize complex motor tasks involving the hand as the handwriting of digits. However, the automatic recognition of words from EMG information has not yet been studied. The aim of this study is to investigate the feasibility of using combined forearm and wrist EMG probes for solving the handwriting recognition problem of 30 words with consolidated machine-learning techniques and aggregating state-of-the-art features extracted in the time and frequency domains. Six healthy subjects, three females and three males aged between 25 and 40 years, were recruited for the study. Two tests in pattern recognition were conducted to assess the possibility of classifying fine hand movements through EMG signals. The first test was designed to assess the feasibility of using consolidated myoelectric control technology with shallow machine-learning methods in the field of handwriting detection. The second test was implemented to assess if specific feature extraction schemes can guarantee high performances with limited complexity of the processing pipeline. Among support vector machine, linear discriminant analysis, and K-nearest neighbours (KNN), the last one showed the best classification performances in the 30-word classification problem, with a mean accuracy of 95% and 85% when using all the features and a specific feature set known as TDAR, respectively. The obtained results confirmed the validity of using combined wrist and forearm EMG data for intelligent handwriting recognition through pattern recognition approaches in real scenarios.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/331152

Scalp Electroencephalogram-Derived Involvement Indexes during a Working Memory Task Performed by Patients with Epilepsy
SENSORS
Autore/i: Iammarino, Erica; Marcantoni, Ilaria; Sbrollini, Agnese; Mortada, MHD Jafar; Morettini, Micaela; Burattini, Laura
Classificazione: 1 Contributo su Rivista
Abstract: Electroencephalography (EEG) wearable devices are particularly suitable for monitoring a subject’s engagement while performing daily cognitive tasks. EEG information provided by wearable devices varies with the location of the electrodes, the suitable location of which can be obtained using standard multi-channel EEG recorders. Cognitive engagement can be assessed during working memory (WM) tasks, testing the mental ability to process information over a short period of time. WM could be impaired in patients with epilepsy. This study aims to evaluate the cognitive engagement of nine patients with epilepsy, coming from a public dataset by Boran et al., during a verbal WM task and to identify the most suitable location of the electrodes for this purpose. Cognitive engagement was evaluated by computing 37 engagement indexes based on the ratio of two or more EEG rhythms assessed by their spectral power. Results show that involvement index trends follow changes in cognitive engagement elicited by the WM task, and, overall, most changes appear most pronounced in the frontal regions, as observed in healthy subjects. Therefore, involvement indexes can reflect cognitive status changes, and frontal regions seem to be the ones to focus on when designing a wearable mental involvement monitoring EEG system, both in physiological and epileptic conditions.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/334072

Cardiorespiratory DB: Collection of cardiorespiratory data acquired during normal breathing, deep breathing and breath holding
DATA IN BRIEF
Autore/i: Sbrollini, A.; Marcantoni, I.; Lunghi, T.; Morettini, M.; Burattini, L.
Classificazione: 1 Contributo su Rivista
Abstract: The database is constituted by 50 datasets containing cardiorespiratory signals acquired from 50 healthy volunteer subjects (one dataset for each subject; 23 males and 27 females; age: 23±5 years) while performing normal breathing, deep breathing, and breath holding, and two spreadsheet files, namely the “SubjectsInfo.xlsx” and “DBInfo.xlsx” containing the metadata of subjects (including demographic data) and of acquired signals, respectively. Cardiorespiratory signals consisted in simultaneously recorded 12-lead electrocardiograms acquired by the clinical M12 Global InstrumentationⓇ digital Holter ECG recorder, and single-lead electrocardiograms and respiration signals acquired by the wearable chest strap BioHarness 3.0 by Zephyr. The database may be useful to: (1) validate the use of wearable sensors in the acquisition of cardiorespiratory data during different respiration kinds, including apnea; (2) investigate the physiological association between cardiovascular and respiratory systems; (3) validate algorithms able to indirectly extract the respiration signal from the electrocardiogram; (4) study the fatigue level induced by a series of controlled respiration patterns; and (5) investigate the effect of COVID-19 infection on the cardiorespiratory system.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/329034

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

A Computer-Aided Screening Solution for the Identification of Diabetic Neuropathy from Standing Balance by Leveraging Multi-Domain Features
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
Autore/i: Mengarelli, A.; Tigrini, A.; Verdini, F.; Scattolini, M.; Mobarak, R.; Burattini, L.; Rabini, R. A.; Fioretti, S.
Classificazione: 1 Contributo su Rivista
Abstract: The early diagnosis of diabetic neuropathy (DN) is fundamental in order to enact timely therapeutic strategies for limiting disease progression. In this work, we explored the suitability of standing balance task for identifying the presence of DN. Further, we proposed two diagnosis pathways in order to succeed in distinguishing between different stages of the disease. We considered a cohort of non-neuropathic (NN), asymptomatic neuropathic (AN), and symptomatic neuropathic (SN) diabetic patients. From the center of pressure (COP), a series of features belonging to different description domains were extracted. In order to exploit the whole information retrievable from COP, a majority voting ensemble was applied to the output of classifiers trained separately on different COP components. The ensemble of kNN classifiers provided over 86% accuracy for the first diagnosis pathway, made by a 3-class classification task for distinguishing between NN, AN, and SN patients. The second pathway offered higher performances, with over 97% accuracy in identifying patients with symptomatic and asymptomatic neuropathy. Notably, in the last case, no asymptomatic patient went undetected. This work showed that properly leveraging all the information that can be mined from COP trajectory recorded during standing balance is effective for achieving reliable DN identification. This work is a step toward a clinical tool for neuropathy diagnosis, also in the early stages of the disease.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/332752

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

Unveiling Muscular Engagement: Evidence of Activity in Mental Imagery and Action Observation
2024 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2024 - Proceedings
Autore/i: Verdini, F.; Capecci, M.; Tigrini, A.; Scattolini, M.; Mobarak, R.; Burattini, L.; Fioretti, S.; Benedetti, M. G.; Ceravolo, M. G.; Mengarelli, A.
Editore: Institute of Electrical and Electronics Engineers Inc.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: The cognitive processes associated with the move-ment representation in the absence of physical execution, specif-ically motor imagery (MI), or with the action observation (AO), have been extensively investigated to understand how these activities can affect the endurance and the physical strength. The observed improvements of the motor performance have been attributed not only to the modifications in the central motor program but also to changes in the peripheral neuronal and musculoskeletal systems. Despite these efforts, capturing these effects has proven challenging, with inconsistent results obtained when analyzing electromyographic activity (EMG). This study aims to explore distinctive myoelectric patterns during MI and AO tasks, contrasting them with baseline patterns associated with a resting condition. Eight healthy subjects participated in imagining and observing a fine motor task involving hand and arm. Surface EMG signals from Opponens Pollicis, Flexor Radialis Carpi and Biceps Brachialis were analyzed for both experimental conditions and compared with signals obtained during rest (REST). Features in the time and the time-frequency domains were computed and two their subsets, resulting statisti-cally different for MI and for AO, in comparison to REST, were selected. These features were then inputted into three shallow machine learning models (KNN, SVM, and LDA) to assess the consistency of muscular engagements. The KNN classifier demonstrated the best performance in terms of accuracy (87 %) for MI vs REST, utilizing a subset of features exclusively defined in the time-domain. To capture muscular activity during AO, a more comprehensive feature subset, including some time-frequency features, was required to KNN to reach an accuracy of 89%.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/333733

Automatic Handwriting Recognition with a Minimal EMG Electrodes Setup: A Preliminary Investigation
2024 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2024 - Proceedings
Autore/i: Tigrini, A.; Ranaldi, S.; Mengarelli, A.; Verdini, F.; Scattolini, M.; Mobarak, R.; Fioretti, S.; Conforto, S.; Burattini, L.
Editore: Institute of Electrical and Electronics Engineers Inc.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Electromyographic (EMG)-based human-machine interfaces showed great potential in hand gesture recognition and recently they have been developed for realizing automatic recognition of handwritten characters or digits. Although smart sensors were commercialized for the forearm, the optimal electrode configuration for high accuracy with minimal channels remains debated. A total of six healthy subjects were asked to write a set of thirty common words of the english vocabulary while EMG signals from forearm and wrist were acquired to perform handwriting recognition using five state of the art feature sets and three pattern recognition models, i.e., K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM). Among the three classifiers the KNN outperformed both LDA and SVM showing a mean accuracy of 71.4% and 96.5 % respectively without and with majority voting post-processor when using combined forearm and wrist EMG information. On the other hand, using only wrist electrodes showed significantly performance drop in all classifiers with accuracy lower than 30.0%. Hence, combining forearm and wrist EMG data is crucial for accurate handwriting recognition with sparse electrode configurations, and limiting the sensing area to the wrist alone may not be sufficient for complex myoelectric decoding tasks. Further research is needed to explore alternative feature sets to improve performance with limited electrode setups.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/333736

Mapping Lower Limb EMG Activity to Ground Reaction Force in Free Walking Condition
2024 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2024 - Proceedings
Autore/i: Mengarelli, A.; Scattolini, M.; Mobarak, R.; Verdini, F.; Fioretti, S.; Burattini, L.; Tigrini, A.
Editore: Institute of Electrical and Electronics Engineers Inc.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Continuous control of lower limb prosthetic and assistive devices is usually performed by mapping electromyography (EMG) activity to joints angular kinematics. However, the inclusion of ground reaction force information within the regulation scheme would be valuable in order to provide a more physiological walking mechanics, thus enhancing patient comfort and usability in real context scenarios. In this paper, we explored the feasibility of a reliable EMG-based estimation of the vertical ground reaction force (VGRF) component during unconstrained walking. The EMG activity from six lower limb muscles was recorded from five healthy subjects who performed four gait trials, and the VGRF was recorded during the stance phase of gait by dynamometric plates. As a regression model, we relied on a LSTM network. Results showed very promising performances for VGRF estimation, without any drop between training and testing (R2 of about 0.90, and a RMSE of 8.7%). The three thigh muscles failed in providing reliable results (R2 <0.7 and RMSE>14%), whereas shank muscles alone provided no significantly different outcomes with respect to using all the muscles, pointing out the possibility of using a reduced EMG recording setup, thus impacting on actual usability. These findings can be considered as a step toward the inclusion of dynamics information in myoelectric volitional control schemes for enhancing movement reconstruction.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/333732

Neuromechanical-Driven Ankle Angular Position Control during Gait Using Minimal Setup and LSTM Model
2024 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2024 - Proceedings
Autore/i: Mobarak, R.; Mengarelli, A.; Verdini, F.; Al-Timemy, A. H.; Fioretti, S.; Burattini, L.; Tigrini, A.
Editore: Institute of Electrical and Electronics Engineers Inc.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Data-driven decoding of lower limb muscles surface electromyography (sEMG) and joints kinematics is a crucial approach for enhancing prosthetic control and assistive reha-bilitation. In this study, three different neuromechanical-driven ankle angle estimation strategies were investigated, i.e. Least Squares - Support Vector Machine (LS-SVM) fed with Time Domain (TD) features, single hidden layer Long Short Term Memory (1HL-LSTM) deep learning model fed within the same features, and 2 hidden layers LSTM (2HL-LSTM) fed with a sequence of raw data windows. The above mentioned schemes were tested with three myoelectric-mechanical combinations, i.e. three thigh muscles (3M), their fusion with hip joint angle (3M+Hip), and Biceps Femoris muscle with the hip (BF+Hip). A combined TD features with 1HL-LSTM has outperformed the other two models in all three input combinations, with an RMSE equal to 2.18 ± 0.44 deg in the case of BF+Hip as input, that appeared to be the best muscles configuration. The results of this study provide a robust ankle angle estimation strategy while preventing the computational cost from being drastically increased. They also highlight the reliability of the BF + Hip combination, thus representing a further step in the advancement of effective control strategies for active ankle prosthesis and rehabilitative devices.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/333737

Leveraging Inertial Information from a Single IMU for Human Daily Activity Recognition
2024 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2024 - Proceedings
Autore/i: Scattolini, M.; Tigrini, A.; Verdini, F.; Iadarola, G.; Spinsante, S.; Fioretti, S.; Burattini, L.; Mengarelli, A.
Editore: Institute of Electrical and Electronics Engineers Inc.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Being able to recognize human activities is important for home monitoring, with the possibility for clinicians to early recognize possible cognitive or physical impairments, but also for rehabilitation and prosthetics control. Nowadays, with the diffusion of wearable devices embedding inertial sensors (IMU) in the market, e.g. bracelets or smartwatches, the attention is moved toward the possibility to automatically recognize daily activities just from this sensor information, including accelerometer and gyroscope. However, the majorities of the studies consider a small set of simple daily activities or high computationally demand models to recognize the chosen tasks. For this reason, the aim of the present study is to consider three shallow machine learning classifiers to recognize 17 different complex activities just from the inertial information of a myo armband, i.e. accelerometer, gyroscope and quaternions. The best performance was reached combining accelerometer and gyroscope features with an accuracy of 98.6% and a LDA model. On the other hand, the KNN seemed to be the most suitable classifier when dealing with quaternion information: accuracy equal to 80% was obtained against 75% of LDA and SVM. Obtained results outperformed classification performances present in the literature, highlighting also a possible role of quaternions for pattern recognition problems.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/333734

Using Adaptive Surface EMG Envelope Extraction for Onset Detection: A Preliminary Study on Upper Limb Amputees
2024 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2024 - Proceedings
Autore/i: Ranaldi, S.; Tigrini, A.; Al-Timemy, A. H.; Verdini, F.; Mengarelli, A.; Schmid, M.; Fioretti, S.; Burattini, L.; Conforto, S.
Editore: Institute of Electrical and Electronics Engineers Inc.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Surface electromyography is a valid and widely used tool for the characterization and the intention of human movement in healthy and pathological subjects; in particular, the thorough identification of the transient phase of EMG activity is an important problem to be solved for both myoelectric control applications for prosthetics and rehabilitation and in movement analysis in general. Among the possible algorithmic solutions, the ones based on the statistical properties of the signal have been considered able to yield stable performance in a variety of different scenarios. In this paper, an adaptive and statistically optimised algorithm for the extraction of the amplitude envelope is exploited for the onset detection from EMG data coming from the shoulder muscles of two upper limb amputees. In particular, onset events have been detected from the optimised point-by-point window length of the adaptive filter, as the instants in which this time series reaches a local minimum, and compared with those coming from visual inspection of accelerometer data from the shoulder. These preliminary results show how using such techniques can yield acceptable performance, supporting the hypothesis of exploiting such an algorithm for the improvement of the performance of myoelectric control algorithms to be applied in clinical contexts.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/333735

The Prognostic Value of Electrocardiographic Alternans in the Primary Prevention on Patients Having an Implantable Cardioverter Defibrillator
2024 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2024 - Proceedings
Autore/i: Iammarino, Erica; Marcantoni, Ilaria; Sbrollini, Agnese; Morettini, Micaela; Swenne, Cees A.; Burattini, Laura
Editore: Institute of Electrical and Electronics Engineers Inc.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Primary prevention therapy with implantable cardioverter defibrillator (ICD) may benefit in specificity evaluating the left ventricular ejection fraction together with other electrocardiogram (ECG)-derived cardiac risk indexes, such as ECG alternans (ECGA). ECGA, the ABAB morphology fluctuation of ECG waves (P wave/QRS complex/T wave), results in P-wave/QRS/T-wave alternans (PWA/QRSA/TWA). This work aims to validate ECGA prognostic value on ECGs acquired from ICD patients (Leiden University Medical Center Database). Thus, 82 controls (ICD therapy was not needed) and 40 cases (ICD therapy was needed) were enrolled. ECGA was analyzed by the enhanced adaptive matched filter method at rest and exercise. Median ECGA features (amplitude-Am, duration-D, area-Ar, magnitude-M) were computed over leads and ICD groups. ECGA ability to discriminate between ICD groups was evaluated by the area under the curve (AUC) of the receiver operating characteristic curve. Precordial leads allowed a better discrimination than all leads (higher AUC). QRSA, at both rest (controls/cases: Am=5/10 μV, D=33/45 beats, Ar=360/760 μV·ms, M=351/603 μV·beats) and exercise (controls/cases: Am=16/20 μV, D=55/57 beats, Ar=1280/1600 μV·ms, M=992/1316 μV·beats) has the best discriminant power, with AUC values higher than 0.7 at rest. ECGA feature normalization by the ECG mean amplitude was also considered. We can conclude that (1) ECGA predictive power is best expressed at rest, and QRSA seems to be the best ECGA form to identify patients who should benefit from primary prevention ICD therapy, (2) normalization seems not to improve our results.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/334092

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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: Background: 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. Methods: At early pregnancy, a cohort of 109 women underwent assessment of maternal biometry and blood tests at fasting, for measurements of several variables (visit 1). Subsequently (26 weeks of gestation) all visit 1 analyses were repeated (visit 2), and a subgroup of women (84 selected) received a 2 h-75 g OGTT (30, 60, 90, and 120 min sampling) with measurement of blood glucose, insulin and C-peptide for reliable assessment of insulin sensitivity (PREDIM index) and insulin secretion/beta-cell function. The dataset was randomly split into 70% training set and 30% test set, and by machine learning approach we identified the optimal model, with TyG included, showing the best relationship with PREDIM. For inclusion in the model, we considered only fasting variables, in agreement with TyG definition. Results: The relationship of TyG with PREDIM was weak. Conversely, the improved TyG, called TyGIS, (linear function of TyG, body weight, lean body mass percentage and fasting insulin) resulted much strongly related to PREDIM, in both training and test sets (R2 > 0.64, p < 0.0001). Bland–Altman analysis and equivalence test confirmed the good performance of TyGIS in terms of association with PREDIM. Different further analyses confirmed TyGIS superiority over TyG. Conclusions: We developed an improved version of TyG, as new surrogate marker of insulin sensitivity in pregnancy (TyGIS). Similarly to TyG, TyGIS relies only on fasting variables, but its performances are remarkably improved than those of TyG.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/307450

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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