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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

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.

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%).

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.

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.

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.

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 fetal electrocardiogram (FECG) is the recording of the electrical activity of the fetal heart. Morphologically, FECG shows the standard P-QRS-TU electrocardiographic pattern and a heart rate (HR) of 120–160 bpm. There are two techniques to acquire FECG: the direct one (DI-FECG), with a spiral wire electrode located on the fetal scalp; and the indirect one (IN-FECG), with electrodes located on the mother’s abdomen. Fetal T-wave alternans (TWA) represents a possible cause of unexplained fetal deaths; its investigation, however, is challenging. This chapter discusses about this scientific and clinical problem, and proposes a procedure to identify TWA from both DI-FECG and IN-FECG. The procedure includes the following 3 steps: (1) automatic identification of fetal R peaks, performed through the improved fetal Pan-Tompkins Algorithm; (2) FECG filtering, performed through linear filtering and the segmented-beat modulation method; and (3) automatic TWA identification, performed through the heart-rate adaptive match filter. Application of this procedure to 5 DI-FECG and 20 IN-FECG from 5 fetuses confirmed its goodness for fetal applications and suggested that fetuses show TWA even when healthy. Moreover, TWA detected in DI-FECG was comparable to that in IN-FECG, suggesting that TWA identification was reliable also in IN-FECG, which is much more affected by artifacts and interferences than DI-FECG.

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.

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

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.

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.

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.

Currently used 24-hour electrocardiogram (ECG) monitors have been shown to skip detecting arrhythmias that may not occur frequently or during standardized ECG test. Hence, online ECG processing and wearable sensing applications have been becoming increasingly popular in the past few years to solve a continuous and long-term ECG monitoring problem. With the increase in the usage of online platforms and wearable devices, there arises a need for increased storage capacity to store and transmit lengthy ECG recordings, offline and over the cloud for continuous monitoring by clinicians. In this work, a discrete cosine transform (DCT) compressed segmented beat modulation method (SBMM) is proposed and its applicability in case of ambulatory ECG monitoring is tested using Massachusetts Institute of Technology–Beth Israel Deaconess Medical Center (MIT–BIH) ECG Compression Test Database containing Holter tape normal sinus rhythm ECG recordings. The method is evaluated using signal-to-noise (SNR) and compression ratio (CR) considering varying levels of signal energy in the reconstructed ECG signal. For denoising, an average SNR of 4.56 dB was achieved representing an average overall decline of 1.68 dBs (37.9%) as compared to the uncompressed signal processing while 95 % of signal energy is intact and quantized at 6 bits for signal storage (CR=2) compared to the original 12 bits, hence resulting in 50% reduction in storage size.

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.

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

Prematurity is a severe condition, usually correlated with critical outcomes. One of the major diseases in preterm infants is bradycardia, defined as the heart rate decreasing under 100 bpm for at least two heartbeats in duration. Usually, bradycardia is considered as a manifestation of immature cardiorespiratory control, but no studies investigated its nature in relation to the different clinical features of preterm infants. Thus, aim of this work is to assess the relation between bradycardia features and the main preterm infant clinical features, weight and gestational age. Ten preterm infants were considered, classified according with three criteria: the weight classification, the gestational age classification and the birth size assessment (that combined the two previous classifications). For each preterm infant, bradycardias are automatically identified and characterized in term of bradycardia features: amplitude, duration and area. Moreover, bradycardia events are classified according with their severity. Finally, bradycardia feature distributions of classes that belong to the same classification criterion were compared. Results seems suggesting that bradycardia features differences are more relevant in preterm infants with different weights than in those with different gestational age, contrary to what expected. Anyway, the best results in term of classification were obtained in the birth size assessment; thus, a combined approach that considers both weight and gestational age is preferable. Moreover, a combined evaluation of amplitude and duration for bradycardia characterization can better assess the severity of this arrhythmia and of the preterm infant clinical status.

Fetal phonocardiography (FPCG) is a non-invasive acoustic recording of fetal heart sounds (fHS). The fHS auscultation plays an important diagnostic role in assessing fetal wellbeing. Typically, FPCG is a non-stationary signal corrupted by the presence of noise. Thus, high-amplitude noise makes detection of FPCG waveforms challenging. Thus, appropriate filtering procedures have to be applied in order to make FPCG clinically usable. In the recent years, Wavelet transformation (WT) filtering has been proposed. In particular, aim of this study is to propose a new method based on WT multi-level decomposition filtering: PCG-Decompositor. To this aim, PCG-Decompositor based on Coiflets mother Wavelet (4th order, 9 levels of decomposition) was applied to 119 real FPCG tracings, all available in Physionet. PCG-Decompositor is a dependent thresholding technique based on FPCG multi-level decomposition analysis. Performances of PCG-Decompositor are computed against soft-thresholding denoising technique (STDT) in terms of Root Mean Square Error (RMSE) and fetal heart rate (fHR). In terms of fHR, PCG-Decompositor and STDT are compared between themselves and also with the so-called annotations, given by the average fHR using a simultaneous cardiotocography analysis. Original signal to noise ratio (SNR) values ranged from 7.1 dB to 24.4 dB; after application of PCG-Decompositor, SNR increased significantly, ranging from 9.7 dB to 26.9 dB (P < 10−7). Moreover, PCG-Decompositor showed a lower dispersion than STDT (RMSE: 0.7 dB vs. 1.2 dB), introduced no FPCG signal delay and left fHR unaltered. Thus, PCG-Decompositor could be a suitable and robust technique to denoise FPCG signals.

Sex differences may assume a key role in condition of impaired glucose metabolism and progression to type 2 diabetes, affecting insulin-dependent processes. However, the presence of sex differences in non-insulin-dependent processes (i.e. glucose effectiveness) has been scarcely investigated. The aim of this study was to detect the presence of sex differences in glucose effectiveness (SG), as assessed by minimal model analysis, in subjects with different degrees of glucose metabolism impairment. Two groups of subjects ranging from normal (NGR, n = 57, males/females: 31/26) to abnormal glucose regulation (AGR, n = 115, males/females 42/73) underwent a 3-h frequently sampled intravenous glucose tolerance test. Minimal model analysis provided SG and its components at zero (GEZI) and at basal (BIE) insulin. Values for SG were 2.52 ± 0.98 10−2 min−1 and 2.81 ± 1.07 10−2 min−1 for males and females in the NGR group, and 2.08 ± 1.21 10−2 min−1 and 2.09 ± 0.98 10−2 min−1 for males and females in the AGR group. No statistically significant difference was found between males and females in both NGR (p = 0.29) and AGR (p = 0.94) groups. Sex differences were not detected for GEZI, which provided the major contribution to SG, either in NGR or AGR group. In conclusion, glucose effectiveness and its components seem to be not affected by sex differences in all glucose tolerance conditions.

Alternans is an electrophysiological phenomenon consisting in a beat-to-beat variation of the morphology of an electrocardiographic (ECG) waveform. Literature has particularly studied T-wave alternans (TWA) because it has been widely recognized as a noninvasive and clinically useful index to predict occurrence of malignant ventricular arrhythmias and, eventually, sudden cardiac death. Historically, alternans of other segments of ECG, like P wave (PWA), or QRS complex (QRSA) gained less interest than TWA, but this is an incomplete vision of the action potential (AP). AP is influenced by electrical activity of all myocardial cells, so it is reasonable that all ECG waveforms could be affected by alternans phenomenon. ECG alternans (ECGA) can be intended as the prevalent nature of alternans. This study aimed to use the heart-rate adaptive match filter (AMF) method, previously applied for TWA applications, to detect ECGA. AMF effectiveness was tested on simulated alternating ECG (alternans-amplitude range: 10 µV–200 µV), characterized by single- and multiple-wave alternans (always of the same amplitude and morphology). AMF method proved to be specific, being able to recognize ECGA absence, and particularly sensitive to TWA. In general, in case of singular-wave alternans, AMF correctly identified the type of alternans and correctly determined its amplitude (mean error: 0%). When TWA was combined to PWA or QRSA, only TWA was identified with an overestimation of its amplitude (mean error: 23%). In conclusion, overall AMF proved its effectiveness and specificity in revealing and discriminating ECGA.

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.

Recurrence quantification analysis (RQA) allows the measurement of signal's regular and chaotic states using recurrence plots instead of deriving information purely from visual analysis. The current study presents RQA of multiple ECG time series simultaneously recorded through different electrodes and depicts the effect of motion artifacts through electrode synchronization and non-synchronization. The ECG data is acquired from a healthy 25-year-old male performing different exercise activities such as standing, walking and jumping. Also, the electrode in every recorded signal is placed at angle offset of 0°, 45° and 90°. The RQA analysis measures recurrence rate (RR), line entropy (ENT) and average diagonal length (L) reveal a highly stable and least chaotic signal in case of standing (RR=0.73, ENT=4.94, L=106.12), somewhat stable and a bit chaotic in case of walking (RR=0.75, ENT=5.35, L=129.13) and least stable and most chaotic in case of subject performing a jump (RR=0.61, ENT=5.07, L=99.16). Secondly, highest and second highest disturbances with respect to exercise movements are observed for electrode combinations (3,4) and (1,4). Distinguishing values for RQA-based measures for different exercise movements suggest that RQA is a powerful tool for differentiation of regular and irregular states occurring due to motion artifacts in the temporal patterns of ECG.

Running is the most common physical activity. Being an aerobic activity, it can act as a trigger for critical cardiac events that may degenerate in sport-related sudden cardiac death. Nowadays, smartphone applications combined with wearable sensors are typically used to monitor runner's performance during training, but almost never to evaluate their cardiac risk conditions. Thus, aim of this study was to propose CaRiSMA as a useful Android application for self-monitoring of cardiac activity of runners while wearing a cardiac sensor and running by strictly following a route around the city of Ancona (6.1 Km). Cardiac data from 10 young runners were recorded and transferred to a smartphone to be analyzed by CaRiSMA, an Android application that provides two traffic lights as output, relative to cardiac health status of the runner and correctness of training intensity. The first traffic light was green in all cases but one for which it was yellow, indicating no risk and increased risk conditions, respectively. The second traffic light was yellow in all cases, suggesting a reduction of the training intensity. In conclusion, CaRiSMA demonstrated to be a potentially useful Android application for self-monitoring of cardiac activity of runners while wearing a cardiac sensor.

The most common sleep disorder is sleep apnea, whose manifestations are long breathing pauses. Sleep apnea assessment is usually performed by polysomnography. During this long-term monitoring, patient respiration and other biosignals are recorded by many sensors, causing a high level of discomfort. Thus, methods able to indirectly estimate the biosignal of interest from the others measured should be preferred. Respiration indirectly measured from electrocardiogram (ECG) is called ECG-derived respiratory (EDR) signal. Recently, Segmented Beat Modulation Method (SBMM) was proposed as a good method for EDR signal estimation in normal breathing. Thus, the aim of this study was to assess the quality of EDR signal estimation by SBMM in pathological events of sleep apnea. With this purpose, sixteen long term polysomnographic recordings from MITBIH Polysomnographic Database were considered. After standard preprocessing, respiration and ECG signals were divided in 30s windows and, in order to match to provided annotations, each window was classified into Normal or Apnea. EDR signal was estimated by SBMM procedure from each ECG window. Respiration and EDR signals were then processed by Fourier analysis to extract respiration frequencies. Respiration frequencies computed from respiration and EDR signals were compared in term of error. Results confirmed the good quality of the estimated EDR signal. Respiration frequency extracted from EDR signal in both Normal (16[13;19]cpm) and Apnea windows (18[15;21]cpm) are equal to those extracted from respiration signal (Normal: 16 [13;19]cpm and Apnea: 18 [15;21]cpm), providing null error distributions. In conclusion, SBMM proved to be a promising tool for EDR signal estimation.

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

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.

Fetal phonocardiography (FPCG) is a non-invasive electronic recording of the acoustic cardiac signals. Unfortunately, FPCG is hidden by high-amplitude noise which makes detection of FPCG waveforms challenging. Aim of the study is to propose PCG-Delineator as an algorithm for automatic detection of the first and second heart sound (S1 and S2, respectively) from FPCG. To this aim, 37 simulated FPCG tracings (Physionet) are filtered by a wavelet-based procedure (4th order Coiflets mother wavelet with 7 decomposition levels) to erase noise. Successively, S1 and S2 are detected. S1 detection procedure is threshold-based (threshold=30% of the filtered FPCG signal maximum amplitude), under the condition that 40ms separate two consecutive S1 sounds. S2 detection procedure is also threshold-based, but under the conditions that S2 has to fall 100ms after preceding S1 and 200ms before successive S1, and that S2 has to have an amplitude lower than 80% that of preceding S1. Sensitivity (SE) and positive predictive values (PPV) were computed. Results indicate that PCG-Delineator was able to reduce noise (our SNR: from −1.1÷7.4dB to 12.9÷17.9dB; 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.

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.

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.

Objective Inflammatory cytokines released by hypertrophic adipocytes contribute to low-grade inflammation, a characteristic of Type 2 Diabetes. Skeletal muscle contraction during physical activity stimulates the secretion of anti-inflammatory cytokines able to counteract this inflammatory status. The aim of this study was to review the evidence of the effectiveness of walking as a physical activity intervention to reduce inflammation. The interplay between adipose tissue and skeletal muscle contributions was also investigated. Method A structured literature review of papers available up to December 2014 was carried out within the PubMed, Scopus and ISI Web of Science databases using the keywords “walking” and “inflammation” in order to identify the studies involving healthy subjects and subjects diagnosed with, or at increased risk of, Type 2 Diabetes. Results Thirty-two studies were reviewed, five investigating the acute effects of walking and twenty-seven its chronic effects (n = 21 interventional and n = 6 observational). Acute effects of walking bouts led to an increase of interleukin-6 in one study, although without any increase in the concentration of the anti-inflammatory marker interleukin-1 receptor antagonist. Eight interventional studies showed a significant reduction of inflammation. A reduction in tumour necrosis factor-α concentration was often associated with an adiposity reduction. The observational studies showed that individuals who walk more present a lower inflammatory status. Conclusion There is no consensus regarding the efficacy of walking in the reduction of low-grade systemic inflammation, even though a relationship cannot be excluded. In each walking bout, no anti-inflammatory effect due to the IL-6-stimulated myokine cascade can be demonstrated., Highlights • Efficacy of walking in the reduction of inflammation is controversial. • Walking efficacy in reducing inflammation is mediated by duration and intensity. • People accustomed to regular walking have lower inflammatory marker concentrations. • No anti-inflammatory effect mediated by muscular IL-6 can be demonstrated.

The beneficial effects of physical activity for the prevention and management of several chronic diseases are widely recognized. Mathematical modeling of the effects of physical exercise in body metabolism and in particular its influence on the control of glucose homeostasis is of primary importance in the development of eHealth monitoring devices for a personalized medicine. Nonetheless, to date only a few mathematical models have been aiming at this specific purpose. We have developed a whole-body computational model of the effects on metabolic homeostasis of a bout of physical exercise. Built upon an existing model, it allows to detail better both subjects’ characteristics and physical exercise, thus determining to a greater extent the dynamics of the hormones and the metabolites considered., Author summary Exercise has a great impact on human metabolism and the lack of physical activity represents one of the main causes of the metabolic disorders. The effectiveness of regular physical activity in the prevention and management of several chronic diseases is widely recognized. In the study of the metabolism and related disorders, mathematical models have proven useful in describing and quantifying physiological processes often not easily measurable in vivo. Formulating a model describing the metabolic responses to a physical exercise session is a challenging task since the effects vary depending on its intensity, duration, modality and are also dependent on the subjects’ physical characteristics (e.g. age, gender, body weight, fitness status). To date, none of the existing computational models is able to provide this level of “personalization”. Thus, starting from an existing model of fuel homeostasis during exercise, we have formulated a novel computational system that is more detailed in describing both the physical exercise and the subjects’ characteristics.

Cardiotocography (CTG) is the most commonly used test for establishing the good health of the fetus during pregnancy and labor. CTG consists in the recording of fetal heart rate (FHR; bpm) and maternal uterine contractions (UC; mmHg). FHR is characterized by baseline, baseline variability, tachycardia, bradycardia, acceleration and decelerations. Instead, UC signal is characterized by presence of contractions and contractions period. Such parameters are usually evaluated by visual inspection. However, visual analysis of CTG recordings has a well-demonstrated poor reproducibility, due to the complexity of physiological phenomena affecting fetal heart rhythm and being related to clinician's experience. Computerized tools in support of clinicians represents a possible solution for improving correctness in CTG interpretation. This paper proposes CTG Analyzer as a graphical tool for automatic and objective analysis of CTG tracings. CTG Analyzer was developed under MATLAB®; it is a very intuitive and user friendly graphical user interface. FHR time series and UC signal are represented one under the other, on a grid with reference lines, as usually done for CTG reports printed on paper. Colors help identification of FHR and UC features. Automatic analysis is based on some unchangeable features definitions provided by the FIGO guidelines, and other arbitrary settings whose default values can be changed by the user. Eventually, CTG Analyzer provides a report file listing all the quantitative results of the analysis. Thus, CTG Analyzer represents a potentially useful graphical tool for automatic and objective analysis of CTG tracings.

Sleep apnea (SA) is linked to cardiovascular complications and to an increased risk of sudden cardiac death. Microvolt T-wave alternans (TWA) is a noninvasive electrocardiographic (ECG) index of cardiovascular risk; its rate of occurrence in SA patients remains unknown. Thus, this study investigated the occurrence of TWA in SA patients during night. To this aim, overnight ECG recordings of 16 SA patients were analyzed for TWA identification by means of our heart rate adaptive match filter. Results indicate that overnight TWA was characterized by a low mean amplitude (mean TWA: 6±3 μV). However, higher-amplitude transient TWA episodes (max TWA: 29±21 μV) occurred overnight, sometimes when patients were awake (max TWA: 33±18 μV; 56% of cases) and sometimes when patients were sleeping (max TWA: 24±23 μV; 44% of cases with 13%, 19%, 6% and 6% during sleep stage 1, 2, 3 and 4, respectively). In only 3 subjects (19%) TWA peaks occurred during an SA episode: during obstructive apnea with arousal in two cases (max TWA of 7 μV and 17 μV, during stages 1 and 2, respectively) and during hypoapnea with arousal in one case (max TWA of 6 μV while awake). Thus, SA patients show significant transient overnight TWA episodes, not necessarily occurring during a SA episode.

Very little is known about the incidence and etiology of fetal T-wave alternans (TWA), an electrophysiologic phenomenon potentially associated to fetal suboptimal outcomes. Thus, availability of automatic methods for quantification of TWA from digital electrocardiograms (ECG) is desirable, since TWA occurrence might indicate the need of taking actions before or during delivery. The heart-rate adaptive match filter (HRAMF) is a well-established method to identify TWA in adult ECG. Aim of the present study was to investigate the possibility of using HRAMF to identify and quantify TWA also in direct fetal ECG (DFECG) recordings. To this aim, HRAMF was applied to 5 min-long DFECG acquired during delivery ("Abdominal and Direct Fetal Electrocardiogram Database" by Physionet) of five healthy fetuses. Significant levels of TWA were measured in all DFECG. Specifically, on average, TWA was quite high in amplitude (9±2 μV) and variable in time, as indicated by values of standard deviation (6±2 μV) and maximum (28±10 μV) of TWA amplitude. Eventually, a positive correlation (ρ=0.68) was observed between maximum TWA and fetal heart rate, even though the limited number of recordings makes this result preliminary. In conclusion, HRAMF proved to be a suitable tool to automatically identify TWA from DFECG.

Fetal phonocardiography (fPCG) is a clinical test to assess fetal wellbeing during pregnancy, labor and delivery. Still, its interpretation may be jeopardized by the presence of noise. Specifically, fPCG is typically corrupted by maternal heart and body organs sounds, fetal movements noise and surrounding environment noise. Thus, appropriate filtering procedures have to be applied in order to make fPCG clinically usable. Wavelet transformation (WT) has been proposed to filter fPCG; however, WT robustness to noise remains unknown. Thus, aim of the present work is to evaluate WT ability and robustness to denoise fPCG characterized by varying signal-to-noise ratios (SNR). To this aim a filtering procedure based on Coiflets mother wavelet (4th order, 7 levels of decomposition) was applied to 37 fPCG simulated tracings, all available in the Simulated Fetal PCGs database by Physionet. Original SNR values ranged from −1.38 dB to 4.54 dB; after application of WT-filtering procedure to fPCG, SNR increased significantly, ranging from 12.95 dB to 17.94 dB (P

Electrocardiography (ECG) and surface electromyography (SEMG) are two non-invasive tests to evaluate cardiac and muscular functionality, respectively. They are both acquired by placing electrodes on the body surface so they become one the interference of the other. Typically, linear filters are used for ECG and SEMG separation: high-pass filters with cutoff at 20 Hz to attenuate ECG interference in SEMG, and low-pass filters with cut-off at 50 Hz to attenuate SEMG interference in ECG. In spite of that, linear filtering is not adequate due to the presence of a 20-50 Hz frequency-band in which the two signal spectra overlap. The aim of the present study was to evaluate the ability of the Segmented-Beat Modulation Method (SBMM) for ECG and SEMG separation and by accurately maintaining signals characteristics. SBMM is a template-based technique for ECG denoising: under the hypothesis of ECG and SEMG linearly superimposed, it first provides an ECG estimation, and then an SEMG estimation by subtraction. In order to test the method under several conditions, SBMM was applied to simulated as well as clinical recordings with superimposed ECG and SEMG. SBMM was able to accurately estimate both ECG and SEMG in all cases. Indeed, ECG and SEMG were estimated by maintain their features such as amplitude (estimation errors

Background: This study aimed to design a simple surrogate marker (i.e., predictor) of the minimal model glucose effectiveness (SG), namely calculated SG (CSG), from a short insulin-modified intravenous glucose tolerance test (IM-IVGTT), and then to ap ply it to study women with previous gestational diabetes mellitus (pGDM). Methods: CSG was designed using the stepwise model selection approach on a population of subjects (n=181) ranging from nor mal tolerance to type 2 diabetes mellitus (T2DM). CSG was then tested on a population of women with pGDM (n=57). Each sub ject underwent a 3-hour IM-IVGTT; women with pGDM were observed early postpartum and after a follow-up period of up to 7 years and classified as progressors (PROG) or non-progressors (NONPROG) to T2DM. The minimal model analysis provided a reference SG. Results: CSG was described as CSG=1.06×10-2+5.71×10-2×KG/Gpeak, KG being the mean slope (absolute value) of loge glucose in 10-25- and 25-50-minute intervals, and Gpeak being the maximum of the glucose curve. Good agreement between CSG and SG in the general population and in the pGDM group, both at baseline and follow-up (even in PROG and NONPROG subgroups), was shown by the Bland-Altman plots (

The Zephyr BioHarness 3.0 (BH3) is a popular wearable system specifically designed for training optimization of professional athletes. BH3 provides the electrocardiogram (ECG BH3) and the heart-rate signal (HRSBH3). Aim of this study is to evaluate the reliability of HRSBH3 to assess its clinical applicability to the general population for cardiac-risk evaluations. Data were acquired from 10 healthy subjects (age: 34±17 years) during a 5-minutes rest. Since the tachogram represents the standard signal for studying the heart rate (HR) and its variability, ECG BH3 was elaborated in order to get the tachogram (HRSTG). HRSBH3 and HRSTG were compared in terms of mean HR (MHR, bpm), HR standard deviation (HRSD, bpm) and HRSD error (bpm). HRSBH3 and HRSTG provided comparable MHR (73.07±15.53 bpm vs 72.86±15.57 bpm, respectively) while HRSD by HRSBH3 was significantly lower than HRSD by HRS TG (4.51 ±2.29 bpm vs 5.63±2.99 bpm, respectively; P=0.0043). HRSD error was significantly greater than zero (0.20–3.00 bpm; P=0.0043); moreover, it was strongly correlated to HRSD by HRS TG (p=0.82, P=0.0036). Thus, HRS BH3 is appropriate only for sport applications based on MHR estimations, but not to clinical evaluations based on HRV measurements.

Based on glucose kinetics minimal model (GKMM) interpretation of frequently sampled intravenous glucose tolerance test (FSIGTT), the aim was to broaden the characterization of insulin-mediated glucose disposal in hypertension by aid of a dynamic insulin sensitivity index, $$ S_{\text{I}}^{\text{D}} $$ , and the related efficiency, $$ \eta = S_{\text{I}}^{\text{D}} /S_{\text{I}} , $$ of the metabolic system to convert the maximal individual response capacity, measured by S I, into an effective insulin control on glucose. The C-peptide minimal model (CPMM) was used to interpret the role of β-cell function. Plasma glucose, insulin, and C-peptide concentrations were measured, during a 5-h FSIGTT, in eighteen normoglycemic individuals: ten hypertensive patients (H-group) and eight normotensive subjects (N-group) with no metabolic syndrome. Compared to our N-group, the H-group showed a significant (P

Sudden cardiac death (SCD) is one of the leading cause of death during sport activities. Heart rate (HR) and HR variability (HRV) provide a measure of how the organism adapts to physical fatigue, and can be monitored by commercial wearable sensors. Still, HR and HRV, widely used to optimize a training session, were rarely used to evaluate the athlete’s health-status, even though widely known to provide indexes of risk for SCD. This work, developed in collaboration with Bio-Medical Engineering Development Srl, aims to provide a contribution to the problem of preventive identification of athletes at increased risk of SCD, by developing and testing a low-cost, large-scale procedure for HR and HRV monitoring from signals obtained using comfortable wearable sensors. To this aim a new protocol for the acquisition of the tachogram was proposed. It included recordings of the signals during resting, exercise and recovery phases, to allow evaluation of prevention as well as performance indexes. The procedure was tested on 10 sedentary subjects (SS) and 10 amateur athletes (AA). Compared to SS, AA showed a better health-status, quantified in a lower resting HR (63 bpm vs. 73 bpm; P < 0.005) and a higher resting HRV (29 ms vs. 23 ms; P < 0.05), and a better performance level, quantifies in a lower recovery time (130 ms vs. 174 ms; P < 0.05). Thus, the proposed procedure allows evaluation of both the health-status and the performance level of an athlete, and represents a valuable tool to contrast SCD in sport.