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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.

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

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

Electrocardiographic alternans, consisting of P-wave alternans (PWA), QRS-complex alternans (QRSA) and Twave 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 mu V) and high (100 mu V) 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 mu V x ms, 762 mu V x ms and 1382 mu V x ms; AAm: 5 mu V, 9 mu V and 7 mu V; 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.

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 disruption. The effect of this novel procedure has been studied numerically, with a simplified two-dimensional 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 concentrations of maximum principal strain and stress have been registered in the proximity of the interface between the physiologic and diseased sphincter, and in those regions where the radial thickness 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 function, and quality of life.

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.

Myocardial bridge (MB) is a congenital heart condition in which a “bridge” of myocardium is overlying a “tunneled” coronary artery. MB can be associated with a series of critical cardiac events. Aim of this study was to evaluate electrocardiographic alternans (ECGA) on a MB patient, being ECGA a cardiac electrical risk index defined as beat-to-beat alternation of electrocardiographic P-wave, QRS-complex and T-wave morphology at stable heart rate. ECGA analysis was performed in a 1-hour 12-lead electrocardiographic recording of a 54 years-old MB male patient at rest by application of the heart-rate adaptive match filter method. Areas of P-wave, QRS and T-wave alternans (PWAA, QRSAA, TWAA) were measured, evaluating also the prevalent among the three. Results showed the prevalent alternans was T-wave alternans, being TWAA on average equal to 6.3 µV×s (PWAA=4.7 µV×s, QRSAA=4.3 µV×s); TWAA prevalence occurrence rate was 94% (PWAA: 5%, QRSAA:1%). TWAA was also found to be significantly correlated (p=0.72, p

The electrocardiographic QT interval is an index of cardiac risk commonly used in clinics. Accurate QT measure is challenging, especially in noisy conditions, when acquisitions of phonocardiograms (PCGs) may be more reliable than acquisitions of electrocardiograms (ECGs). However, PCG features are less used in clinics. Thus, aim of the study was to propose a model for indirectly measuring the electrocardiographic QT interval from the phonocardiographic heart sounds in healthy subjects. To this aim, simultaneously acquired PCGs and ECGs of 99 healthy subjects were processed to obtain median PCG and ECG beats. Beat length, S1 onset and S2 onset were identified from the median PCG beat, while QT interval (QT) was measured from the median ECG beat. Then, a regression model was formulated by regression analysis to obtain PCG-based QT estimation (QT) and validated by leave-one-out cross-validation. Correlation coefficient (p) and estimation error were also computed. QT and QT did not differ significantly (model formulation: 362ms vs 358ms; model validation:360ms vs 358ms, respectively; P>0.5) and were significantly correlated (model formulation: p=0.7, p −13 ; model validation: p=0.6, P −10 ); median error is 1 ms (

The proposed dataset provides annotations for the 552 cardiotocographic (CTG) recordings included in the publicly available "CTU-CHB intra-partum CTG database" from Physionet (https://physionet.org/content/ctu-uhb-ctgdb/1.0.0/). Each CTG recording is composed by two simultaneously acquired signals: i) the fetal heart rate (FHR) and ii) the maternal tocogram (representing uterine activity). Annotations consist in the detection of starting and ending points of specific CTG events on both FHR signal and maternal tocogram. Annotated events for the FHR signal are the bradycardia, tachycardia, acceleration and deceleration episodes. Annotated events for the maternal tocogram are the uterine contractions. The dataset also reports classification of each deceleration as early, late, variable or prolonged, in relation to the presence of a uterine contraction. Annotations were obtained by an expert gynecologist with the support of CTG Analyzer, a dedicated software application for automatic analysis of digital CTG recordings. These annotations can be useful in the development, testing and comparison of algorithms for the automatic analysis of digital CTG recordings, which can make CTG interpretation more objective and independent from clinician's experience.

Beat classification and denoising are two challenging and fundamental operations when processing digital electrocardiograms (ECG). This paper proposes the extended segmented beat modulation method (ESBMM) as a tool for automatic beat classification and ECG denoising. ESBMM includes four main steps: (1) beat identification and segmentation into PQRS and TU segments, (2) wavelet-based time-frequency feature extraction, (3) convolutional neural network-based classification to discriminate among normal (N), supraventricular (S), and ventricular (V) beats, and (4) a template-based denoising procedure. ESBMM was tested using the MIT&ndash, BIH arrhythmia database available at Physionet. Overall, the classification accuracy was 91.5% while the positive predictive values were 92.8%, 95.6%, and 83.6%, for N, S, and V classes, respectively. The signal-to-noise ratio improvement after filtering was between 0.15 dB and 2.66 dB, with a median value equal to 0.99 dB, which is significantly higher than 0 (p &lt, 0.05). Thus, ESBMM proved to be a reliable tool to classify cardiac beats into N, S, and V classes and to denoise ECG tracings.

Atrial fibrillation (AF) is a common cardiac disorder that can cause severe complications. AF diagnosis is typically based on the electrocardiogram (ECG) evaluation in hospitals or in clinical facilities. The aim of the present work is to propose a new artificial neural network for reliable AF identification in ECGs acquired through portable devices. A supervised fully connected artificial neural network (RSL_ANN), receiving 19 ECG features (11 morphological, 4 on F waves and 4 on heart-rate variability (HRV)) in input and discriminating between AF and non-AF classes in output, was created using the repeated structuring and learning (RSL) procedure. RSL_ANN was created and tested on 8028 (training: 4493, validation: 1125, testing: 2410) annotated ECGs belonging to the &ldquo, AF Classification from a Short Single Lead ECG Recording&rdquo, database and acquired with the portable KARDIA device by AliveCor. RSL_ANN performance was evaluated in terms of area under the curve (AUC) and confidence intervals (CIs) of the received operating characteristic. RSL_ANN performance was very good and very similar in training, validation and testing datasets. AUC was 91.1% (CI: 89.1%&ndash, 93.0%), 90.2% (CI: 86.2%&ndash, 94.3%) and 90.8% (CI: 88.1%&ndash, 93.5%) for the training, validation and testing datasets, respectively. Thus, RSL_ANN is a promising tool for reliable identification of AF in ECGs acquired by portable devices.

The database here described contains data of integrated surveillance for the "Coronavirus disease 2019" (abbreviated as COVID-19 by the World Health Organization) in Italy, caused by the novel coronavirus SARS-CoV-2. The database, included in a main folder called COVID-19, has been designed and created by the Italian Civil Protection Department, which currently manages it. The database consists of six folders called 'aree' (containing charts of geographical areas interested by containment measures), 'dati-andamento-nazionale' (containing data relating to the national trend of SARS-CoV-2 spread), 'dati-json' (containing data that summarize the national, provincial and regional trends of SARS-CoV-2 spread), 'dati-province' (containing data relating to the provincial trend of SARS-CoV-2 spread), 'dati-regioni' (containing data relating to the regional trend of SARS-CoV-2 spread) and 'schede-riepilogative' (containing summary sheets relating to the provincial and regional trends of SARS-CoV-2 spread). The Italian Civil Protection Department daily receives data by the Italian Ministry of Health, analyzes them and updates the database. Thus, the database is subject to daily updates and integrations. The database is freely accessible (CC-BY-4.0 license) at https://github.com/pcm-dpc/COVID-19. This database is useful to provide insight on the spread mechanism of SARS-CoV-2, to support organizations in the evaluation of the efficiency of current prevention and control measures, and to support governments in the future prevention decisions.

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

Background In the prehospital triage of patients presenting with symptoms suggestive of acute myocardial ischemia, reliable myocardial ischemia detection in the electrocardiogram (ECG) is pivotal. Due to large interindividual variability and overlap between ischemic and nonischemic ECG‐patterns, incorporation of a previous elective (reference) ECG may improve accuracy. The aim of the current study was to explore the potential value of serial ECG analysis using subtraction electrocardiography. Methods SUBTRACT is a multicenter retrospective observational study, including patients who were prehospitally evaluated for acute myocardial ischemia. For each patient, an elective previously recorded reference ECG was subtracted from the ambulance ECG. Patients were classified as myocardial ischemia cases or controls, based on the in‐hospital diagnosis. The diagnostic performance of subtraction electrocardiography was tested using logistic regression of 28 variables describing the differences between the reference and ambulance ECGs. The Uni‐G ECG Analysis Program was used for state‐of‐the‐art single‐ECG interpretation of the ambulance ECG. Results In 1,229 patients, the mean area‐under‐the‐curve of subtraction electrocardiography was 0.80 (95%CI: 0.77–0.82). The performance of our new method was comparable to single‐ECG analysis using the Uni‐G algorithm: sensitivities were 66% versus 67% (p‐value > .05), respectively; specificities were 80% versus 81% (p‐value > .05), respectively. Conclusions In our initial exploration, the diagnostic performance of subtraction electrocardiography for the detection of acute myocardial ischemia proved equal to that of state‐of‐the‐art automated single‐ECG analysis by the Uni‐G algorithm. Possibly, refinement of both algorithms, or even integration of the two, could surpass current electrocardiographic myocardial ischemia detection.

Minimal model analysis of intravenous glucose tolerance test (IVGTT) data represents the reference method to assess insulin sensitivity (SI) and glucose effectiveness (SG) that quantify the insulin-dependent and insulin-independent processes of glucose disappearance, respectively. However, test duration (3 h) and need for modeling expertise limit the applicability of this method. Aim of this study was providing a simple predictor of SG applicable to short test (1 h), as previously done with SI. Three groups of subjects reflecting different glucose tolerance degrees underwent a 3 h IVGTT: subjects with normal glucose tolerance (NGT, n = 164), with defective glucose regulation (DGR, n = 191), and with type 2 diabetes (T2D, n = 39). Minimal model analysis provided reference SG and its components at zero (GEZI) and basal (BIE) insulin. The simple predictor CSG (calculated SG) was described by the formula CSG = α0 + α1 × KG/Gpeak, being KG the glucose disappearance rate (between 10 and 50 min) and Gpeak the maximum of the glucose curve during the test; α0 and α1 coefficients were provided by linear regression analysis. CSG and SG showed a markedly significant relationship in the whole dataset (r = 0.72, p

The present work proposes an interactive software tool for surface electromyography signal processing and analysis, focused on the assessment of muscular activations during walking and easy...

Testing balance through squatting exercise is a central part of many rehabilitation programs and sports and plays also an important role in clinical evaluation of residual motor ability. The assessment of center of pressure (CoP) displacement and its parametrization is commonly used to describe and analyze squat movement and the laboratory-grade force plates (FP) are the gold standard for measuring balance performances from a dynamic view-point. However, the Nintendo Wii Balance Board (NWBB) has been recently proposed as an inexpensive and easily available device for measuring ground reaction force and CoP displacement in standing balance tasks. Thus, this study aimed to compare the NWBB-CoP data with those obtained from a laboratory FP during a dynamic motor task, such as the squat task. CoP data of forty-eight subjects were acquired simultaneously from a NWBB and a FP and the analyses were performed over the descending squatting phase. Outcomes showed a very high correlation (r) and limited root-mean-square differences between CoP trajectories in anterior-posterior (r 0.99, 1.63 ± 1.27 mm) and medial-lateral (r 0.98, 1.01 ± 0.75 mm) direction. Spatial parameters computed from CoP displacement and ground reaction force peak presented fixed biases between NWBB and FP. Errors showed a high consistency (standard deviation 2.4% of the FP outcomes) and a random spread distribution around the mean difference. Mean velocity is the only parameter which exhibited a tendency towards proportional values. Findings of this study suggested the NWBB as a valid device for the assessment and parametrization of CoP displacement during squatting movement.

Background and objective Cardiotocography (CTG), consisting in the simultaneous recording of fetal heart rate (FHR) and maternal uterine contractions (UC), is a popular clinical test to assess fetal health status. Typically, CTG machines provide paper reports that are visually interpreted by clinicians. Consequently, visual CTG interpretation depends on clinician's experience and has a poor reproducibility. The lack of databases containing digital CTG signals has limited number and importance of retrospective studies finalized to set up procedures for automatic CTG analysis that could contrast visual CTG interpretation subjectivity. In order to help overcoming this problem, this study proposes an electronic procedure, termed eCTG, to extract digital CTG signals from digital CTG images, possibly obtainable by scanning paper CTG reports. Methods eCTG was specifically designed to extract digital CTG signals from digital CTG images. It includes four main steps: pre-processing, Otsu's global thresholding, signal extraction and signal calibration. Its validation was performed by means of the “CTU-UHB Intrapartum Cardiotocography Database” by Physionet, that contains digital signals of 552 CTG recordings. Using MATLAB, each signal was plotted and saved as a digital image that was then submitted to eCTG. Digital CTG signals extracted by eCTG were eventually compared to corresponding signals directly available in the database. Comparison occurred in terms of signal similarity (evaluated by the correlation coefficient ρ, and the mean signal error MSE) and clinical features (including FHR baseline and variability; number, amplitude and duration of tachycardia, bradycardia, acceleration and deceleration episodes; number of early, variable, late and prolonged decelerations; and UC number, amplitude, duration and period). Results The value of ρ between eCTG and reference signals was 0.85 (P Conclusions eCTG procedure is a promising useful tool to accurately extract digital FHR and UC signals from digital CTG images.

Background: Surface electromyography (SEMG) is widely used in clinics for assessing muscle functionality. All procedures proposed for noise reduction alter SEMG spectrum, especially in the low-frequency band (below 30 Hz). Indeed, low-frequency band is generally addressed to motion artifacts and electrocardiogram (ECG) interference without any further investigation on the possibility of SEMG having significant spectral content. The aim of the present study was evaluating SEMG frequency content to understand if low-frequency spectral content is negligible or, on the contrary, represents a significant SEMG portion potentially providing relevant clinical information. Method: Isometric recordings of five muscles (sternocleidomastoideus, erectores spinae at L4, rectus abdominis, rectus femoris and tibialis anterior) were acquired in 10 young healthy voluntary subjects. These recordings were not affected by motion artifacts by construction and were pre-processed by the Segmented-Beat Modulation Method for ECG deletion before performing spectral analysis. Results: Results indicated that SEMG frequency content is muscle and subject dependent. Overall, the 50th[25th;75th] percentiles spectrum median frequency and spectral power below 30 Hz were 74[54; 87] Hz and 18[10; 31] % of total (0–450 Hz) spectral power. Conclusions: Low-frequency spectral content represents a significant SEMG portion and should not be neglected. Keywords: Surface electromyographic signal, Electromyographic spectrum, Segmented-Beat Modulation Method, Non-linear filtering, Spectral analysis

Background:Sport-related sudden cardiac death (SRSCD) can only be fought through prevention.Objective:The aim of this study is to propose an innovative software application, CaRiSMA 1.0 (Cardiac Risk Self-Monitoring Assessment), as a potential tool to help contrasting SRSCD and educating to a correct training.Methods:CaRiSMA 1.0 analyzes the electrocardiographic and heart-rate (HR) signals acquired during a training session through wearable sensors and provides intuitive graphical outputs consisting of two traffic lights, one related to cardiac health, based on resting QTc (a parameter quantifying the duration of ventricular contraction and subsequent relaxation), and one related to training, based on exercise HR. Safe and worthwhile training sessions have green traffic lights. A red QTc traffic light indicates the need of a medical consultation, whereas a red HR traffic light indicate the need of a reduction of training intensity. By way of example, CaRiSMA 1.0 was applied to sample data acquired in 10 volunteers (age= 27±11 years; males/females 3/7).Results:Two acquisitions (20.0%) were rejected because too noisy, indicating that wearable sensors may record poor quality signals. The QTc traffic light was red in 1 case, indicating that people practicing sport may not be aware of being at risk. The HR traffic light was red in 0 cases.Conclusion:CaRiSMA 1.0 is a software application that, for the first time in the sport context, uses QTc, the most important index of cardiac risk in clinics. Thus, it has the potential for giving a contribution in the fight against SRSCD.

Natural variability of myoelectric activity during walking was recently analyzed considering hundreds of strides. This allowed assessing a parameter seldom considered in classic surface EMG (sEMG) studies: the occurrence frequency, defined as the frequency each muscle activation occurs with, quantified by the number of strides when a muscle is recruited with that specific activation modality. Aim of present study was to propose the occurrence frequency as a new parameter for assessing sEMG-signal variability during walking. Aim was addressed by processing sEMG signals acquired from Gastrocnemius Lateralis, Tibialis Anterior, Rectus Femoris and Biceps femoris in 40 healthy subjects in order to: (1) show that occurrence frequency is not correlated with ON/OFF instants (R mean = 0.11 ± 0.07; P > 0.05) and total time of activation (R mean = 0.15 ± 0.08; P > 0.05); (2) confirm the above results by two handy examples of application (analysis of gender and age) which highlighted that significant (P

Background: Indirect fetal electrocardiography is preferable to direct fetal electrocardiography because of being noninvasive and is applicable also during the end of pregnancy, besides labor. Still, the former is strongly affected by noise so that even R-peak detection (which is essential for fetal heart-rate evaluations and subsequent processing procedures) is challenging. Some fetal studies have applied the Pan-Tompkins’ algorithm that, however, was originally designed for adult applications. Thus, this work evaluated the Pan-Tompkins’ algorithm suitability for fetal applications, and proposed fetal adjustments and optimizations to improve it. Method: Both Pan-Tompkins’ algorithm and its improved version were applied to the “Abdominal and Direct Fetal Electrocardiogram Database” and to the “Noninvasive Fetal Electrocardiography Database” of Physionet. R-peak detection accuracy was quantified by computation of positive-predictive value, sensitivity and F1 score. Results: When applied to “Abdominal and Direct Fetal Electrocardiogram Database”, the accuracy of the improved fetal Pan-Tompkins’ algorithm was significantly higher than the standard (positive-predictive value: 0.94 vs. 0.79; sensitivity: 0.95 vs. 0.80; F1 score: 0.94 vs. 0.79; Pvs. 0.55, Pvs. 0.46, P=0.23; F1 score: 0.60 vs. 0.47, P=0.11). Conclusion: In indirect fetal electrocardiographic applications, improved fetal Pan-Tompkins’ algorithm is to be preferred over the standard, since it provides higher R-peak detection accuracy for heart-rate evaluations and subsequent processing.

The study aimed to assess possible differences between healthy, young males and females in co-contraction activity of tibialis anterior and gastrocnemius lateralis during gait at self-selected speed and cadence. The Statistical-Gait-Analysis methodology, allowing a statistical characterization of gait by averaging spatial-temporal and sEMG-based parameters over hundreds of strides per walking trial, was applied on thirty age-matched subjects: 15 males and 15 females. Co-contractions were assessed as the overlapping periods between muscular activities. Results showed the occurrence of four different co-contractions during gait cycle, for both groups. No significant differences in activation instants (ON-OFF) were detected between groups. Otherwise, all the co-contractions occurred in higher number of strides (%) in females, respect to males: in early stance (40.7 ± 18.7% vs. 18.9 ± 11.0%, p

Background Sudden infant death syndrome is more frequent in preterm infants (PTI) than term infants and may be due to cardiac repolarization instability, which may manifest as T-wave alternans (TWA) on the electrocardiogram (ECG). Therefore, the aim of the present work was to analyze TWA in nonpathological PTI and to open an issue on its physiological interpretation. Methods Clinical population consisted of ten nonpathological PTI (gestational age ranging from 29 3 7 to 34 2 7 weeks; birth weight ranging from 0.84 to 2.10 kg) from whom ECG recordings were obtained ("Preterm infant cardio-respiratory signals database" by Physionet). TWA was identified through the heart-rate adapting match filter method and characterized in terms of mean amplitude values (TWAA). TWA correlation with several other clinical and ECG features, among which gestational age-birth weight ratio, RR interval, heart-rate variability, and QT interval, was also performed. Results TWA was variable among infants (TWAA = 26 ± 11 µV). Significant correlations were found between TWAA versus birth weight (ρ = -0.72, p = .02), TWAA versus gestational age-birth weight ratio (ρ = 0.76, p = .02) and TWAA versus heart-rate variability (ρ = -0.71, p = .02). Conclusions Our preliminary retrospective study suggests that nonpathological PTI show TWA of few tens of µV, the interpretation of which is still an open issue but could indicate a condition of cardiac risk possibly related to the low development status of the infant. Further investigations are needed to solve this issue.

Dofetilide is an antiarrhythmic drug that selectively inhibits the rapid component of the delayed rectifier potassium current. The administration of dofetilide may cause ventricular arrhythmias and torsade de pointes. Electrocardiographic (ECG) microvolt T-wave alternans (TWA), an electrophysiologic phenomenon consisting in the beat-to-beat alternation of the T-wave amplitude requiring computerized algorithms to be detected, has also been associated to malignant ventricular arrhythmias. Aim of the present study was to evaluate if dofetilide induces TWA during the 24 hours following administration. The study population consisted of 22 healthy subjects ("ECG Effects of Ranolazine, Dofetilide, Verapamil, and Quinidine in Healthy Subjects" database by Physionet) to whom a 500 μg-dose of dofetilide was administered. For each subject, 10 s ECG were acquired at baseline (0.5 hour before dofetilide administration) and at 15 time points during the 24 hours following the drug administration. ECG were then processed for automatic TWA detection by correlation method. In 21 subjects out of 22, after dofetilide administration, TWA significantly increased to a peak value (median TWA values went from 6 μV at baseline to a max 32 μV; p; 0.05), on average after 5 hours, to then come back to values closer to baseline. Thus, in healthy subjects, dofetilide increases occurrence and levels (6 times baseline value on average) of TWA in the hours following its administration.

Background and aims Insulin clearance is a relevant process in glucose homeostasis. In this observational study, we aimed to assess insulin clearance (ClINS) in women with former gestational diabetes (fGDM) both early after delivery and after a follow-up. Methods and results We analysed 59 fGDM women, and 16 women not developing GDM (CNT). All women underwent an oral glucose tolerance test (OGTT) yearly, and an insulin-modified intravenous glucose tolerance test (IVGTT) at baseline and at follow-up end (until 7 years). Both IVGTT and OGTT ClINS was assessed as insulin secretion to plasma insulin ratio. We also defined IVGTT first (0–10 min) and second phase (10–180 min) ClINS. We found that 14 fGDM women progressed to type 2 diabetes (PROG), whereas 45 women remained diabetes-free (NONPROG). At baseline, IVGTT ClINS showed alterations in PROG, especially in second phase (0.88 ± 0.10 l·min−1 in PROG, 0.60 ± 0.06 in NONPROG, 0.54 ± 0.07 in CNT, p ≤ 0.03). Differences in ClINS were not found from OGTT. Cox regression analysis showed second phase ClINS as significant type 2 diabetes predictor (hazard ratio = 1.90, 95% confidence interval 1.09–3.30, p = 0.02). Conclusion This study showed that insulin clearance derived from an insulin-modified IVGTT is notably altered in women with history of GDM progressing towards type 2 diabetes.

Background and objectives An Error related Potential (ErrP) can be noninvasively and directly measured from the scalp through electroencephalography (EEG), as response, when a person realizes they are making an error during a task (as a consequence of a cognitive error performed from the user). It has been shown that ErrPs can be automatically detected with time-discrete feedback tasks, which are widely applied in the Brain-Computer Interface (BCI) field for error correction or adaptation. In this work, a semi-supervised algorithm, namely the Functional Source Separation (FSS), is proposed to estimate a spatial filter for learning the ErrPs and to enhance the evoked potentials. Methods EEG data recorded on six subjects were used to evaluate the proposed method based on FFS algorithm in comparison with the xDAWN algorithm. FSS- and xDAWN-based methods were compared also to the Cz and FCz single channel. Single-trial classification was considered to evaluate the performances of the approaches. (Both the approaches were evaluated on single-trial classification of EEGs.) Results The results presented using the Bayesian Linear Discriminant Analysis (BLDA) classifier, show that FSS (accuracy 0.92, sensitivity 0.95, specificity 0.81, F1-score 0.95) overcomes the other methods (Cz - accuracy 0.72, sensitivity 0.74, specificity 0.63, F1-score 0.74; FCz - accuracy 0.72, sensitivity 0.75, specificity 0.61, F1-score 0.75; xDAWN - accuracy 0.75, sensitivity 0.79, specificity 0.61, F1-score 0.79) in terms of single-trial classification. Conclusions The proposed FSS-based method increases the single-trial detection accuracy of ErrPs with respect to both single channel (Cz, FCz) and xDAWN spatial filter.

Sport Database is a collection of 126 cardiorespiratory data, acquired through wearable sensors from 81 subjects while practicing 10 different sports. Each cardiorespiratory dataset consists of demographic info (gender, age, weight, height, smoking habit, alcohol consumption and weekly training rate), cardiorespiratory signals (electrocardiogram, heart-rate series, RR-interval series and breathing-rate series) and training notes. Demographic info was collected by survey. Cardiorespiratory signals were acquired through the chest strap BioHarness 3.0 by Zephyr. Eventually, training notes including the sport-dependent training protocol, were manually annotated. Sport Database may be useful to support: 1) the investigation of cardiorespiratory system adaptations to different types of physical exercise; 2) the development of automatic algorithms finalized to real-time health monitoring of athletes and preventive identification of subjects at increased risk of sport-related sudden cardiac death; and, 3) clinical testing of the BioHarness 3.0 by Zephyr. Further acquisitions could involve other sports, other cardiovascular signals and/or parameters, data from different biological systems, and other acquisition devices. Keywords: Sport acquisition, Exercise, Electrocardiogram, Heart-rate series, Breathing-rate series

A recently developed software application, eCTG, extracts cardiotocographic (CTG) signals from digital CTG images, possibly obtained by scanning paper CTG reports. The aim of this study was to evaluate eCTG robustness across varying image formats, resolution and screw. Using 552 digital CTG signals from the &ldquo, CTU-UHB Intrapartum Cardiotocography Database&rdquo, of Physionet, seven sets of digital CTG images were created, differing in format (.TIFF, .PNG and .JPEG), resolution (96 dpi, 300 dpi and 600 dpi) and screw (0.0&deg, 0.5&deg, and 1.0&deg, ). All created images were submitted to eCTG for CTG signals extraction. Quality of extracted signals was statistically evaluated based 1) on signal morphology, by computation of the correlation coefficient (&rho, ) and of the mean signal error percent (MSE%), and 2) on signal clinical content, by assessment of 18 standard CTG variables. For all sets of images, &rho, was high (&rho, &ge, 0.81) and MSE% was small (MSE% &le, 2%). However, significant changes occurred in median values of four, four and five standard CTG variables in image sets with 96 dpi resolution, 0.5&deg, screw and 1.0&deg, screw, respectively. In conclusion, for an optimal eCTG performance, digital images should be saved in lossless formats, have a resolution of at least 300 dpi and not be affected by screw.

The proposed dataset provides a complete set of simultaneously acquired data from contactless and wearable devices for direct and indirect heart-rate measurement. Data were acquired on a total of 20 healthy white Caucasian subjects wearing no makeup (10 males and 10 females; age: 22.50 ± 1.57 years; height: 173 ± 10 cm; weight: 62.80 ± 9.52 kg) and consisted of: i) videos of the subject's face acquired by a RGB-D (Red, Green, Blue and Depth) camera (Microsoft Kinect v2), which is a contactless device; ii) electrocardiographic (ECG) recordings acquired by a clinical Holter ECG recorder (Global Instrumentation's M12R Holter), which is a wearable device; and iii) heart-rate measurements acquired from a commercial smartwatch (Moto 360 smartwatch by Motorola), which is also a wearable device. ECG recordings were processed to extract the R-peaks position and obtain a reference indirect measurement of the heart rate. A direct measurement of the heart rate was provided by the commercial smartwatch. The dataset here presented could be useful to develop new algorithms for heart-rate detection from contactless devices and to validate contactless heart-rate estimation in comparison to reference heart rate from clinical wearable devices and to heart rate from commercial wearable devices. Keywords: Heart-rate measurement, Contactless sensing, RGB-D sensors, Holter electrocardiography, Wearable sensor

Early prediction of target patients at high risk of developing Type 2 diabetes (T2D) plays a significant role in preventing the onset of overt disease and its associated comorbidities. Although fundamental in early phases of T2D natural history, insulin resistance is not usually quantified by General Practitioners (GPs). Triglyceride-glucose (TyG) index has been proven useful in clinical studies for quantifying insulin resistance and for the early identification of individuals at T2D risk but still not applied by GPs for diagnostic purposes. The aim of this study is to propose a multiple instance learning boosting algorithm (MIL-Boost) for creating a predictive model capable of early prediction of worsening insulin resistance (low vs high T2D risk) in terms of TyG index. The MIL-Boost is applied to past electronic health record (EHR) patients' information stored by a single GP. The proposed MIL-Boost algorithm proved to be effective in dealing with this task, by performing better than the other state-of-the-art ML competitors (Recall from 0.70 and up to 0.83). The proposed MIL-based approach is able to extract hidden patterns from past EHR temporal data, even not directly exploiting triglycerides and glucose measurements. The major advantages of our method can be found in its ability to model the temporal evolution of longitudinal EHR data while dealing with small sample size and variability in the observations (e.g., a small variable number of prescriptions for non-hospitalized patients). The proposed algorithm may represent the main core of a clinical decision support system.

BACKGROUND: Human ether‐à‐go‐go‐related gene (hERG) potassium‐channel block represents a harmful side effect of drug therapy that may cause torsade de pointes (TdP). Analysis of ventricular repolarization through electrocardiographic T‐wave features represents a noninvasive way to accurately evaluate the TdP risk in drug‐safety studies. This study proposes an artificial neural network (ANN) for noninvasive electrocardiography‐based classification of the hERG potassium‐channel block. METHODS: The data were taken from the “ECG Effects of Ranolazine, Dofetilide, Verapamil, and Quinidine in Healthy Subjects” Physionet database; they consisted of median vector magnitude (VM) beats of 22 healthy subjects receiving a single 500 μg dose of dofetilide. Fourteen VM beats were considered for each subject, relative to time‐points ranging from 0.5 hr before to 14.0 hr after dofetilide administration. For each VM, changes in two indexes accounting for the early and the late phases of repolarization, ΔERD (30%) and ΔT(S) (/A), respectively, were computed as difference between values at each postdose time‐point and the predose time‐point. Thus, the dataset contained 286 ΔERD (30%)‐ΔT(S) (/A) pairs, partitioned into training, validation, and test sets (114, 29, and 143 pairs, respectively) and used as inputs of a two‐layer feedforward ANN with two target classes: high block (HB) and low block (LB). Optimal ANN (OANN) was identified using the training and validation sets and tested on the test set. RESULTS: Test set area under the receiver operating characteristic was 0.91; sensitivity, specificity, accuracy, and precision were 0.93, 0.83, 0.92, and 0.96, respectively. CONCLUSION: OANN represents a reliable tool for noninvasive assessment of the hERG potassium‐channel block.

BACKGROUND Obesity is known to induce a deterioration of insulin sensitivity (SI ), one of the insulin-dependent components of glucose tolerance. However, few studies investigated whether obesity affects also the insulin-independent component, that is glucose effectiveness (SG ). This cross-sectional study aimed to analyse SG and its components in different body mass index (BMI) categories. MATERIALS AND METHODS Three groups of subjects spanning different BMI (kg m-2 ) categories underwent a 3-h frequently sampled intravenous glucose tolerance test: Lean (LE; 18.5 ≤ BMI

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

The research purpose was to quantify the co-contraction patterns of quadriceps femoris (QF) vs. hamstring muscles during free walking, in terms of onset-offset muscular activation, excitation intensity, and occurrence frequency. Statistical gait analysis was performed on surface-EMG signals from vastus lateralis (VL), rectus femoris (RF), and medial hamstrings (MH), in 16315 strides walked by 30 healthy young adults. Results showed full superimpositions of MH with both VL and RF activity from terminal swing, 80 to 100% of gait cycle (GC), to the successive loading response (≈0–15% of GC), in around 90% of the considered strides. A further superimposition was detected during the push-off phase both between VL and MH activation intervals (38.6 ± 12.8% to 44.1 ± 9.6% of GC) in 21.9 ± 13.6% of strides, and between RF and MH activation intervals (45.9 ± 5.3% to 50.7 ± 9.7 of GC) in 32.7 ± 15.1% of strides. These findings led to identify three different co-contractions among QF and hamstring muscles during able-bodied walking: in early stance (in ≈90% of strides), in push-off (in 25–30% of strides) and in terminal swing (in ≈90% of strides). The co-contraction in terminal swing is the one with the highest levels of muscle excitation intensity. To our knowledge, this analysis represents the first attempt for quantification of QF/hamstring muscles co-contraction in young healthy subjects during normal gait, able to include the physiological variability of the phenomenon.

Clinical utility of an electrocardiogram (ECG) affected by too high levels of noise such as baseline wanders, electrode motion artifacts, muscular artifacts and power-line interference may be jeopardized if not opportunely processed. Template-based techniques have been proposed for ECG estimation from noisy recordings, but usually they do not reproduce physiological ECG variability, which, however, provides clinically useful information on the patient's health. Thus, this study proposes the Segmented-Beat Modulation Method (SBMM) as a new template-based filtering procedure able to reproduce ECG variability, and assesses SBMM robustness to the aforementioned noises in comparison to a standard template method (STM). SBMM performs a unique ECG segmentation into QRS segment and TUP segment, and successively modulates/demodulates (by stretching or compressing) the former segments in order to adaptively adjust each estimated beat to its original morphology and duration. Consequently, SBMM estimates ECG with significantly lower estimation errors than STM when applied to recordings affected by various levels of the considered noises (SBMM: 176-232µV and 79-499µV; STM: 215-496µV and 93-1056µV, for QRS and TUP segments, respectively). Thus, SBMM is able to reproduce ECG variability and is more robust to noise than STM.

The study was designed to assess the co-contractions of tibialis anterior (TA) and gastrocnemius lateralis (GL) in healthy school-age children during gait at self-selected speed and cadence, in terms of variability of onset-offset muscular activation and occurrence frequency.Statistical gait analysis, a recent methodology performing a statistical characterization of gait by averaging spatio-temporal and sEMG-based parameters over numerous strides, was performed in 100 healthy children, aged 6-11 years. Co-contractions were assessed as the period of overlap between activation intervals of TA and GL.On average, 165±27 strides were analyzed for each child, resulting in approximately 16,500 strides. Results showed that GL and TA act as pure agonist/antagonists for ankle plantar/dorsiflexion (no co-contractions) in only 19.2±10.4% of strides. In the remaining strides, statistically significant (p0.05) co-contractions appear in early stance (46.5±23.0% of the strides), mid-stance (28.8±15.9%), pre-swing (15.2±9.2%), and swing (73.2±22.6%). This significantly increased complexity in muscle recruitment strategy beyond the activation as pure ankle plantar/dorsiflexors, suggests that in healthy children co-contractions are likely functional to further physiological tasks as balance improvement and control of joint stability.This study represents the first attempt for the development in healthy children of a normative dataset for GL/TA co-contractions during gait, achieved on an exceptionally large number of strides in every child and in total. The present reference frame could be useful for discriminating physiological and pathological behavior in children and for designing more focused studies on the maturation of gait.

Electrocardiographic (ECG) T-wave endpoint (Tend) identification suffers lack of reliability due to the presence of noise and variability among leads. Tend identification can be improved by using global repolarization waveforms obtained by combining several leads. The dominant T-wave (DTW) is a global repolarization waveform that proved to improve Tend identification when computed using the 15 (I to III, aVr, aVl, aVf, V1 to V6, X, Y, Z) leads usually available in clinics, of which only 8 (I, II, V1 to V6) are independent. The aim of the present study was to evaluate if the 8 independent leads are sufficient to obtain a DTW which allows a reliable Tend identification. To this aim Tend measures automatically identified from 15-dependent-lead DTWs of 46 control healthy subjects (CHS) and 103 acute myocardial infarction patients (AMIP) were compared with those obtained from 8-independent-lead DTWs. Results indicate that Tend distributions have not statistically different median values (CHS: 340 ms vs. 340 ms, respectively; AMIP: 325 ms vs. 320 ms, respectively), besides being strongly correlated (CHS: ρ=0.97, AMIP: 0.88; P-27). Thus, measuring Tend from the 15-dependent-lead DTWs is statistically equivalent to measuring Tend from the 8-independent-lead DTWs. In conclusion, for the clinical purpose of automatic Tend identification from DTW, the 8 independent leads can be used without a statistically significant loss of accuracy but with a significant decrement of computational effort. The lead dependence of 7 out of 15 leads does not introduce a significant bias in the Tend determination from 15 dependent lead DTWs.

A markerless low cost system for the estimation of some spatial-temporal parameters of human gait.A system usable on not-instrumented treadmill.A system characterized by a very high level of automation.A system can be used to analyze up to one hundred steps.A system with precision comparable with the gold standard. Background and objectiveA markerless low cost prototype has been developed for the determination of some spatio-temporal parameters of human gait: step-length, step-width and cadence have been considered. Only a smartphone and a high-definition webcam have been used. MethodsThe signals obtained by the accelerometer embedded in the smartphone are used to recognize the heel strike events, while the feet positions are calculated through image processing of the webcam stream. Step length and width are computed during gait trials on a treadmill at various speeds (3, 4 and 5km/h). ResultsSix subjects have been tested for a total of 504 steps. Results were compared with those obtained by a stereo-photogrammetric system (Elite, BTS Engineering). The maximum average errors were 3.7cm (5.36%) for the right step length and 1.63cm (15.16%) for the right step width at 5km/h. The maximum average error for step duration was 0.02s (1.69%) at 5km/h for the right steps. ConclusionThe system is characterized by a very high level of automation that allows its use by non-expert users in non-structured environments. A low cost system able to automatically provide a reliable and repeatable evaluation of some gait events and parameters during treadmill walking, is relevant also from a clinical point of view because it allows the analysis of hundreds of steps and consequently an analysis of their variability.

Fetal phonocardiogram (FPCG) consists in the recording of fetal heart sounds by means of a sensor placed on the mother’s abdominal surface. Usually, FPCG includes two major sounds for each fetal cardiac cycle: S1, produced by the sudden closure of mitral and tricuspid valves, and S2 produced by the closure of aortic and pulmonary valves. The aim of the present study was to propose AdvFPCG-Delineator for automatic fetal S1 and S2 identification and to demonstrate its reliability in different clinical conditions. The method consists of a wavelet-based filtering procedure followed by the computation of the scalogram, from which S1 and S2 were identified using a threshold-based algorithm. AdvFPCG-Delineator was tested on the “Simulated Fetal PCGs database” (37 FPCG signals) and on the experimental “Shiraz University fetal heart sounds database” (119 FPCG signals), both available at PhysioNet ( https://physionet.org ). Manual S1 and S2 annotations and simultaneously acquired cardiotocographic recordings were used to compute reference fetal heart rate (FHR) for the simulated and experimental databases, respectively. No statistically significant difference was observed between estimated vs reference FHR (140 bpm vs 140 bpm, respectively) for the simulated database, for which AdvFPCG-Delineator was also able to track beat-to-beat variability (correlation over 92%). Additionally, no statistically significant difference was observed between estimated vs reference FHR (141 bpm vs 140 bpm, respectively) for the experimental database, even when stratifying by clinical conditions (maternal age, gestational age, etc.). In conclusion, AdvFPCG-Delineator proved to be a reliable method to automatically identify S1 and S2 from fetal phonocardiograms.

Background Insulin resistance is an early-stage deterioration of Type 2 diabetes. Identification and quantification of insulin resistance requires specific blood tests; however, the triglyceride-glucose (TyG) index can provide a surrogate assessment from routine Electronic Health Record (EHR) data. Since insulin resistance is a multi-factorial condition, to improve its characterisation, this study aims to discover non-trivial clinical factors in EHR data to determine where the insulin-resistance condition is encoded. Methods We proposed a high-interpretable Machine Learning approach (i.e., ensemble Regression Forest combined with data imputation strategies), named TyG-er. We applied three different experimental procedures to test TyG-er reliability on the Italian Federation of General Practitioners dataset, named FIMMG_ obs dataset, which is publicly available and reflects the clinical use-case (i.e., not all laboratory exams are prescribed on a regular basis over time). Results Results detected non-conventional clinical factors (i.e., uricemia, leukocytes, gamma-glutamyltransferase and protein profile) and provided novel insight into the best combination of clinical factors for detecting early glucose tolerance deterioration. The robustness of these extracted clinical factors was confirmed by the high agreement (from 0.664 to 0.911 of Lin's correlation coefficient ( r c )) of the TyG-er approach among different experimental procedures. Moreover, the results of the three experimental procedures outlined the predictive power of the TyG-er approach (up to a mean absolute error of 5.68 % and r c = 0.666 , p . 05 ). Conclusions The TyG-er approach is able to carry information about the identification of the TyG index, strictly correlated with the insulin-resistance condition, while extracting the most relevant non-glycemic features from routine data.

Background. Larger one-time values of spatial QRS-T angle (SA) are associated with risk. However, experience how serial changes in SA $(\Delta SA)$ should be interpreted is lacking. Even within normal limits, any $\Delta SA$ likely signifies electrical remodeling. This study aimed to assess the impact of choosing either $\Delta SA$ or $\vert \Delta SA\vert$ as one of a set of serial ECG difference features that constitute the input for our deep learning serial-ECG classifier (DLSEC). Methods. DLSEC was trained and tested to detect emerging pathology in two serial ECG databases: a heart failure database and an acute ischemia database. Either $\Delta SA$ or $\vert \Delta SA\vert$ were among 13 features of serial-ECG differences. DLSEC was dynamically generated during learning, and testing area under the curve (AUC) of the receiver operating characteristic was computed. Results. The DLSECs performed well in emerging heart failure as well as in acute ischemia: testing AUCs were 72% and 84% for the heart failure database and 77% and 83% for the ischemia database, for $\Delta SA$ or $\vert \Delta SA\vert$ among the features, respectively. Conclusion. $\vert \Delta SA\vert$ among the features was superior to $\Delta SA$ in discriminating cases and controls. Our study supports the concept that any $\Delta SA$ , irrespective of its sign, indicates a worsening clinical condition. Further corroboration requires studies in other clinical situations.

A heart affected by atrial fibrillation (AF) presents atrial cells that depolarize in many sites, generating a chaotic electrical activity. On the electrocardiogram (ECG), this activity reflects in the appearance of fibrillatory (F) waves, consisting of low-amplitude oscillations at 4–10 Hz. Aim of the present study is to propose an automatic AF identification method based on F-wave frequency analysis in 10 s ECGs. To this aim, 10 s ECG from 90 healthy subjects (HSs) and 50 AF patients (AFPs) were considered. ECGs were processed by the segmented beat modulation method to reduce components in the F-wave band. Then, the power spectral density (PSD) was computed and the F-wave frequency ratio (FWFR), defined as the ratio between the spectral area in the F-wave frequency band and the total spectral area, was computed. FWFR ability to discriminate AFPs from HSs was evaluated by analyzing the area under the curve (AUC) of the receiver operating characteristic, and by computation of sensitivity, specificity and accuracy. FWFR values were higher in AFPs than in HSs (p

Epilepsy is a chronic neurological disorder, hallmark of which is unpredictable epileptic seizures (ES). The leading cause of death in people with uncontrolled ES is the sudden unexpected death in epilepsy (SUDEP), which is believed to share genes with sudden cardiac death (SCD). Being T-wave alternans (TWA) an ECG index of SCD, aim of this work was to evaluate TWA occurrence in proximity of ES. Electrocardiograms (ECG) from five partial epileptic patients constituting the “Post-Ictal Heart Rate Oscillations in Partial Epilepsy” Database by Physionet were analysed for automatic TWA identification by the heart-rate adaptive match filter (HRAMF). ES onsets and offsets were annotated. ECG segments starting 10min before ES and ending 10min after ES were extracted and further processed to characterize trends of median heart rate, median TWA (mTWA) and maximum TWA (MTWA) in ES proximity. Levels of mTWA were significantly higher than what previously observed in a female healthy population in all ES (46[25; 59]μV), pre-ES (31 [25;62]μ V) and post-ES (30[26;63]μ V) conditions. Both mTWA and MTWA tended to increase during ES. Thus, in proximity of ES, our epileptic patients are at increased risk of SCD, possibly associated with SUDEP. Other studies defining TWA role as a biomarker for SUDEP are needed.

Sport-related sudden cardiac death (SRSCD), defined as “death occurring during sport or within one hour of cessation of training”, is the leading cause of death in athletes. SRSCD occurs in the presence of underlying cardiovascular diseases, some of which may be identified by processing electrocardiographic recordings acquired during training (TECGs). A fast and accurate processing of TECGs during or immediately after training is challenging since TECGs are typically highly corrupted by noise and interferences, which may jeopardize their interpretation and identification of abnormal morphologies. The present study evaluated the ability of GPU-based Segmented-Beat Modulation Method (GPU-SBMM) to provide a noise-free estimation of TECGs, and to improve the algorithm by GPU acceleration to make it compatible with modern hardware. In this research, 19 6-to-10 min TECGs (sampling frequency: 256 Hz), acquired from 8 subjects while performing 4 different exercise tasks (walk, run, low-resistance bike and high-resistance bike), were analyzed. Results indicate that GPU-SBMM application yielded a significant increase of SNR(dB) (from 1 ±5 dB to 19±5 dB; p

T-wave alternans (TWA) is an every-other-beat fluctuation of the T-wave amplitude, often at microvolt (invisible) levels. It is recognized as an important risk index of severe ventricular arrhythmias, leading sometimes to sudden cardiac arrest. Many algorithms for automatic detection and quantification of TWA have been proposed; when applied to the same electrocardiogram (ECG), they may provide a different TWA quantification, making interpretation of differences difficult. Aim of this work is to propose TWA Simulator as a useful tool to validate and compare TWA identification methods. TWA Simulator is a user-friendly MATLAB graphical user interface (GUI) able to generate, model, visualize and store simulated ECG (SECG) affected by TWA of known morphology and amplitude. SECG is constructed by a N-fold repetition of a template, constituted by a real and clean ECG beat. Both number of beats and RR inter-beat variability can be set by the user. Both direct and inverted TWA can be simulated. Direct TWA is simulated by adding a waveform (among four possibility) to every other T wave; inverted TWA is simulated by changing T-wave polarity in every-other SECG beat. Availability of TWA Simulator would allow efficient validation and comparison of automatic TWA identification methods by helping interpretation of results.

Respiration rate and variability are indicators of health-condition changes. In chronic disease management, it is becoming increasingly desirable to use wearable devices in order to minimize invasiveness and maximize comfort. However, not all wearable devices integrate sensors for direct acquisition of respiratory (DAR) signal. In these cases, the breathing extraction can be done through indirect methods, typically from the electrocardiogram (ECG). The aim of the present study is to propose a single-ECG-lead procedure based on the Segmented-Beat Modulation Method (SBMM) as a suitable tool for ECG-derived respiratory (EDR) signal estimation and respiration frequency (RF) identification. Clinical data consisted of combined measurements of two-lead (I and II) ECG and DAR signals from 20 healthy subjects ('CEBS' database by Physionet). Each respiration-affected ECG lead was submitted to a specifically designed SBMMbased procedure for EDR estimation by ECG subtraction. RF from EDR and DAR were identified as the frequency at which the Fourier spectrum has a maximum in the 0.07-1.00 Hz frequency range. Results indicated that mean RF values over the population from EDR signals ($0.27 \pm 0.09$ Hz and $0.27 \pm 0.09$ Hz from leads I and II, respectively) were not significantly different from that from DAR ($0.28 \pm 0.09$ Hz). Moreover, differences in RF identification ($0.01 \pm 0.03$ Hz and $0.00 \pm 0.02$ Hz from leads I and II, respectively) were, on average not significantly different from 0. Thus, SBMM-based procedure is robust and accurate for EDR estimation and RF identification.

Fetal T-wave alternans (TWA) is a still littleknown marker for severe fetus-heart instabilities and may be related to some currently unjustified fetal deaths. Automatically detecting TWA on direct fetal electrocardiograms (DFECG) means possibility of providing fetuses the right treatment during delivery. Instead, automatically identifying TWA on indirect fetal electrocardiograms (IFECG) means possibility of providing fetuses the right treatment even during pregnancy, when taking actions for outcome improvement is still possible. Moreover, TWA identification from IFECG is noninvasive, and thus safe for both fetuses and mothers. The aim of this work was testing the heart-rate adaptive match filter (HRAMF) for automatic TWA identification in IFECG and comparing HRAMF performance in IFECG against DFECG. To this aim, simultaneously recorded DFECG and IFECG tracings from 5 healthy fetuses were used ("Abdominal and Direct Fetal Electrocardiogram Database" from Physionet). TWA measurements (frequency, mean amplitude, maximum amplitude, and amplitude standard deviation) in IFECG (1.09±0.04 Hz, 11±5 μV, 21±12 μV and 7±3 μV) were of the same order of magnitude of those in DFECG (1.07±0.02 Hz, 9±2 μV, 30±11 μV and 6±2 μV). Moreover, a direct correlation (ñ) was found between maximum TWA and fetal heart rate (IFECG: ρ=0.999; P=0.022; DEFEG: ρ=0.642; P=0.243). Thus, HRAMF was able to detect TWA from IFECG as well as from DFECG.

Cardiotocography (CTG) consists in the simultaneous recording of two distinct traces, the fetal heart rate (FHR; bpm) and the maternal uterine contractions (UCs; mmHg). CTG analysis consists in the evaluation of specific features of traces, among which fetal decelerations (DECs) are considered the "center-stage" since possibly related to fetal distress. DECs are classified based on their duration and occurrence in relation to UCs as prolonged, early, late and variable; each class associates to a specific status of the fetus health. Typically, CTG traces are visually interpreted; however, computerized CTG analysis may overcome subjectivity in CTG interpretation. Thus, this study proposes a new automatic algorithm for computerized identification and classification of DECs. The algorithm was tested on the 552 CTG recordings constituting the "CTU-CHB intra-partum CTG database" of Physionet. Of these, 470 (85.15%) were found suitable for automatic DECs identification and classification. Overall, 5888 DECs were identified, of which 3255 (55.28%) were classified while the other 2633 (44.72%) remained unclassified due to very strict preliminary classification criteria (now required for avoiding misclassifications). Among the classified DECs, 468 (14.38%) were classified as prolonged, 1498 (46.02%) as early, 32 (0.98%) as late, 1257 (38.62%) as variable. Thus, among the classified DECs, the most common are the early and the variable ones (overall 84.64%), the occurrence of which ranged from 0 to 14 DECs per recording. These findings are in agreement with what reported in literature. In conclusion, the proposed algorithm for automatic DECs identification and classification represents a useful tool for computerized CTG analysis.

Synergic and antagonist actions of intrinsic and extrinsic foot muscles are acknowledged during walking of adult people. Conversely, reciprocal role of these muscles in children walking has not been adequately deepened yet. Purpose of the study was the surface-EMG-based evaluation of mutual recruitment of intrinsic and extrinsic foot muscles during healthy-children walking. Extensor digitorum brevis (EDB) is one of the main intrinsic foot muscles, controlling foot movement and stability. In this study, EDB and tibialis anterior (TA) were considered as representative of intrinsic and extrinsic foot muscles, respectively. Surface-EMG signals during 4-minute walking trial were acquired in eight healthy school-age children (mean±SD: age 8.3±1.7 years; height 136±8 cm; mass 30.9±6.2 kg) to fulfill the goal of the study. Then, Statistical gait analysis, a recent methodology performing a statistical characterization of gait, was applied to process EMG data. Data from an exceptional number of strides were acquired and analyzed to consider the expected variability (mean±SD = 265±30 strides for each child, nearly 2500 in total). Results showed that: 1) the most frequent recruitment of EDB muscle occurred between 25% and 55% of gait cycle, i.e., in Flat foot contact and Push off phases; 2) the most frequent recruitment of TA occurred from 55% to following 10% of gait cycle, i.e., during Swing and Heel strike phases. This indicated that the most significant EDB activity overlapped the region of no activity (or infrequent activity) for TA and vice versa. In conclusion, present study suggests that TA and EDB muscles acted mainly as antagonist muscles for foot/ankle-joint movement during children walking. These findings highlighted the need of measuring the mutual activity of extrinsic vs. intrinsic foot muscles as tool for a deeper understanding of mechanisms regulating ankle-foot stability and as a possible marker of pathology of neuro-muscular system.