Miracle-dz/UniFAR-FTUs huggingface dataset:
Age and Changes in Extracted Features of Lagged Poincare Plot

The Poincare plot is a geometrical representation of RR time series constructed by plotting successive RR intervals on a 2D phase space. In this article, the impact of age on the shape of Poincare plot of RR intervals and extracted features for quantification of this space is considered. Fantasia database from Physionet databank is used in this paper. Two hours of ECG recording (sampling frequency: 250 Hz) for twenty young (21–34 years old) and twenty older adults (68–85 years old) were used while all subjects remained in a resting state. After extraction of RR intervals from ECG, Poincare plot with 10 different lags (1–10) were constructed for each RR series, and eleven different features were extracted for each lag. Extracted features from lagged Poincare plot were used as input to K-nearest neighbor classifier to discriminate between two groups of young and older adults. Sensitivity of 86.5%, specificity of 95.1%, and the accuracy of 91.4% was achieved in the classification.
Inward Rectifier Current Downregulation Promotes Spontaneous Calcium Release in a Novel Model of Rat Ventricular Electrophysiology

Aberrant intracellular calcium handling, as observed in diseases such as heart failure, promotes lethal ventricular arrhythmias and sudden cardiac death. Recent data from our laboratory suggests that reduced expression of the inward rectifier current in failing rat myocytes increases spontaneous calcium release, however existing computational models are unable to reproduce the underlying stochastic calcium cycling dynamics and so we have been unable to use simulation approaches to explore the cause of this pro-arrhythmic behaviour. Here, we develop a novel model of rat ventricular electrophysiology that reproduces normal spatio-temporal calcium dynamics. Simulations implementing a similar reduction in inward rectifier current to that observed experimentally show that spontaneous calcium release is promoted by action potential prolongation and sarcoplasmic reticulum loading in the presence of a depolarised resting membrane potential. Combined, these effects can result in triggered activity. The model therefore provides insight into arrhythmogenic mechanisms in failing ventricular myocytes and can be utilised to further explore pro-arrhythmic behaviour caused by abnormal calcium handling.
Deep Learning Based QRS Multilead Delineator in Electrocardiogram Signals

The surface electrocardiogram (ECG) is the most widely adopted test to diagnose cardiac diseases. Extracting critical biomarkers from these signals, such as the QRS width, requires delineating the fundamental waves in them. However, even though ECG signals significantly change depending on the recording methodology and cardiac condition, the available QRS delineators are hard to adapt to non-considered cases. We present a deep learning-based multilead ECG delineation method which can successfully delineate QRS complexes. Our approach reached root-mean-square errors (RMSE) of 12.1±0.5 and 18.5±1.1 ms for QRS onset and offset, respectively, when evaluated on the QT database; thus, demonstrating to be comparable to the state-of-the-art. Moreover, these results are similar to the RMSE calculated from differences between the two cardiologists that annotated this database, namely, 14.7 ms for the QRS onset and 17.2 ms for the offset.
Predictive Models for Risk Assessment of Worsening Events in Chronic Heart Failure Patients

This work aims at developing and assessing a machine learning based Knowledge Discovery task for risk prediction of major cardiovascular worsening events in chronic heart failure patients. Clinical data from 50patients with chronic heartfailure was analyzed. For each patient, personal data, different vital and clinical parameters and the presence of cardiovascular worsening events have been stored every three months per two years. We defined the Knowledge Discovery analysis as a predictive task stated as supervised binary classification problem. The class label was defined based on the occurrence or not of cardiovascular worsening events between two consecutive visits. To take into account the temporality of the worsening events, six different temporal weighting strategies, applied to the vital parameters, were tested. Several machine learning algorithms were applied for each strategy obtaining different predictive models. Models performance have been evaluated mainly in term of area under the ROC curve (AUC), and Linear Support Vector Machine got the best performing predictive model. The implemented Knowledge Discovery task have shown to be a reliable tool for support cardiologists for riskpredictions of major cardiovascular worsening events.
Development and Validation of an In Silico Rabbit Purkinje Cell Action Potential Model: A Step Towards a Drug Safety Testing Tool

Companies and regulators evaluate compounds' cardiac safety commonly through in-vitro assays involving cardiac cells. Rabbit cardiac Purkinje Cells (RPCs) are very sensitive to drug effects such as Action Potential Duration (APD) alteration by ion channel block. Our objective is to create a novel RPC model calibrated with the latest experimental data. We developed a new RPC model using Pan-Rudy‘s Canine Purkinje cell model as a framework. We adapted important parameters, namely, size, ion fluxes and conductances. We also included a sodium current Markovian formulation to account for its recently increased importance. Variables were adjusted to match the main experimentally observed RPCs features. Steady-state was reached by pacing the model for 1500 seconds at different Basic Cycle Lengths (BCLs). The model was validated by extracting data from experimental sources. Results show overall agreement with the literature with improvements to previous models. In conclusion, we improved and successfully validated an RPC model. This work paves the way towards a reliable in-silico tool for testing drug effects on RPCs.
Simultaneous Electrical and Fluorescence Recording of HL-1 cells' Electrical Activity in Response to Extracellular Calcium Stimulation

In current cell tissue engineering and cardiology, beating models with behavior and properties similar to humans are necessary. The cardiac muscle HL-1 cells, whose genes have a parent with adult atrial myocytes, serve as a suitable model. HL-1 cells were seeded on a 120-electrode microelectrode (MEA) chamber and transient transfection of Accelerated Sensor of Action Potentials 1 (ASAP1), a genetically encoded voltage indicator, was performed. Simultaneous electrical and optical recording of cardiac cell culture electrical activity was made when the cells started to produce spontaneous action potentials. Recording synchronization was made using TTL pulses. Results showed that HL-1 cells start to produce asynchronous spontaneous action potentials $(-375\pm 10\mu V)$ when reaching 90% MEA chamber confluency and which become periodical addition of extracellular calcium $(-501\pm 14\ \mu V,\ 2.1\pm 1.0\ Hz)$. The ASAP1's fluorescent response reached up to ${21}\pm{5}\%\ \Delta F/F$. The time constant between detection of electrical and fluorescent response $(\tau_{e/0})$ was determined as $12\pm 5$ ms.
The HRV Multifractality Spectrum and not the Power Spectrum Is Altered in Paraplegic Individuals With Low-Level Lesion

The autonomic nervous system may contribute to the multifractal-multiscale dynamics of heart rate variability (HRV). Aim of this work is to evaluate multifractal-multiscale features of HRV complexity when the integrative autonomic regulation is altered. We recorded R-R intervals for 10 minutes in 14 spinal cord injured (SCI) individuals with lesion below the 12th thoracic vertebra, which have intact autonomic cardiac innervations with partially altered autonomic integrative control, and in 34 matched able-bodied (AB) controls. We calculated powers spectra by FFT and generalized Hurst coefficients, $\alpha(q,\tau)$, with scales $\tau$ between 6 and 120 seconds and q between −4 and +4, by multifractal multiscale detrended fluctuation analysis. At each scale we calculated $\alpha SD(\tau)$ standard deviation of $\alpha(q, \tau)$. $\alpha SD(\tau)$ is ≥0 and tends to zero at scales where the series is monofractal. AB and SCI groups were compared by Mann-Whitney test. While SCI and AB spectra were similar, $\alpha SD(\tau)$ was significantly lower at $\tau < 7\ s$ and at $\tau$ around 24 s in the SCI group. Thus, in low-level SCI individuals, the HRV multifractal-multiscale detrended fluctuation analysis reveals alterations in the autonomic integrative control, likely due to loss of multifractality at specific scales, that are completely undetected by traditional spectral analysis.
Multiple P-wave Morphologies in Paroxysmal Atrial Fibrillation Patients During Sinus Rhythm: A Simulation Study

P-wave in electrocardiographic (ECG) signals represents the activation of the atrial substrate. Its shape is influenced by the origin of the sinus rhythm (SR) and the conduction of each part of the atria and thus the analysis of its morphology (PWM) can reveal information related to the atrial conduction routes. Recently, the analysis of the PWM from PAF patients during SR, revealed the presence of a distinct secondary morphology in a remarkable percentage of beats. We hypothesize that the variability on the PWM can be attributed to the coexistence of electrical remodeling and the transient modification of the conduction route which is the result of the shift of the stimulation site within the sinus node. In this study, the Courtemanche et al. model was incorporated in a 2D tissue of the left and the right atrium, while tissue heterogeneities were considered. Simulations were conducted, using the CHASTE platform, in normal and electrical remodeled tissue. In each simulation, the stimulation site was shifted within the sinus node. The results show that in electrical remodeled tissue, the shift of the stimulation site led to significant changes in the activation time in the atria compared with the healthy tissue. In future, the use of a 3D model of human atria is necessary in order to investigate the reproducibility of the results while the conditions under which the pacemaker location shifts must be also revealed.
Classification of Atrial Fibrillation Using Stacked Auto Encoders Neural Networks

In this paper, a combination of deep learning method called stacked auto encoder with the aim of classifying atrial fibrillation (AF) is utilized. An electrocardiogram (ECG) signals from MIT-BIH database are used and spectral, time and non-linear features are extracted from this signal. First extracted features were evaluated using statistical test, analysis of variance (ANOVA) and selected significant features then used for stacked auto encoder as parallel form to classify AF and normal samples. Then, final decision performed using the ensemble averaging method. The average accuracy for classifying AF and normal samples were 95.5%.
Interactive Exploration of Left Atrium Population-Level Morphology in Atrial Fibrillation Patients

We have developed computational methods for interactively exploring the shape of the left-atrium in a population of atrial fibrillation patients. We analyze the LA shape through a shape-learning algorithm termed as particle-based modeling (PBM), in which we extract surface contours from a population of images and then parameterize population-level shape statistics through the automatic placement of a dense set of homologous landmark positions (aka correspondences) using an optimization on information content. We then generate a 2-D embedding of the resulting high-dimensional dataset which allows us to visualize the data on a scatter plot, with each data point representing a single sample. This parameterization of the shape characteristics of samples collapsed onto a single plot gives us a visual representation of the population-level morphology of the data. Cardiac MR angiography data from 212 AF patients was collected retrospectively from a database of AF patients at the University of Utah. From the 2-D scatter plot, we were able to interactively select individual samples, view their shapes, and see associated clinical data. We can also map new patients to infer their relations to other patients in the population via querying nearby samples and viewing their clinical data.
Development of New Left Atrial Anatomical Models For the Study of the Left Atrial Appendage Implications in Atrial Fibrillation

Atrial fibrillation is associated with a five-fold increase of the stroke risk. Left atrial appendage (LAA) is the atrial site with the highest blood stasis risk, increasing thrombus formation and stroke. Recent studies have been focused on the association between the left atrial appendage anatomical features and the stroke risk. However, conflicting results have been published. In this context, clinical studies suggested the stroke risk stratification could be improved by using haemodynamics information on the left atrium and mainly on the left atrial appendage. Therefore, the aim of this study was the design and development of a method which enabled to reconstruct and generate several LA anatomical models, where each one was characterized by a different LAA morphology. These anatomical models represent the computational domain for the computational fluid dynamics simulations of the haemodynamics within the left atrium and LAA.
Adding Two Dimensions to Heart Rate Variability Research

Introduction: Heart rate variability (HRV) analysis tools have been mainly available for analysis of human electrocardiographic derived heart rate. We explore extending HRV analysis to two additional dimensions: (1) analysis across multiple mammalian species and (2) analysis across different levels of integration for example sinoatrial tissue. Methods: We analyzed the beating rate variability (BRV) across the two additional dimensions using the PhysioZoo computer program that we recently introduced. We used published databases of electrocardiograms from four mammal types: human $(n=18)$, dog $(n=17)$, rabbit $(n=4)$ and mouse $(n=8)$. We computed the BRV measures for each. We also show how the PhysioZoo program can be used for the analysis of sinoatrial node tissue BRV. Results: The study of typical mammalian heart and respiration rates (obtained from the dominant high frequency peak) revealed a linear relationship between these two quantities. Analysis of the rabbit sinoatrial node tissue BRV showed that it had reduced overall variability when compared to in vivo heart BRV.
A Temporal Area Variation Regularized Deep Learning Network for Left Ventricle Segmentation on CMR

The automated segmentation of the left ventricular (LV) on MRI is a crucial step for the evaluation of LV structure and function. However, LV segmentation is still a challenging task, mainly because of inherent difficulties from the variable imaging conditions. Hence this study aims to propose an innovative approach to segment LV endocardium and epicardium based on the phenomena of temporal area variation correlation. The proposed method is three-fold: (1) For the first time, we formulated a significant phenomenon that epicardium and endocardium have same area variation tendency into a temporal area variation constraint. (2) We designed a deep leaning network based on RNN to model such a temporal area variation constraint. (3) An efficient optimization framework was developed to achieve end-to-end optimization. The deep learning network was trained and validated on cardiac MRI datasets from MICCAI 2012 LV segmentation challenge including 100 patients (50 train patients and 50 test patients).
A Learning Based Statistical Approach for Combining Multiple Measurements in Electrocardiographic Imaging

Kalman filter has been applied in literature to inverse electrocardiography problem as a spatio-temporal method. However, there is still an open question of how the essential parameters in the state-space representation are found without claiming strong assumptions. In this study, we proposed a maximum likelihood (ML) estimation based method which incorporates multiple body surface measurements to estimate the parameters that are essential to use Kalman filter. Our proposed approach, Maximum Likelihood Inference & Filtering (MLIF), was compared with zero order Tikhonov regularization and Bayesian maximum a posteriori estimation (BMAP) by using three different training sets under two different measurement noise levels. The results showed that mean correlation coefficient (CC) for Tikhonov regularization is 0.60, and mean CC ranges 0.64 to 0.82, and 0.66 to 0.99 for Bayesian MAP and MLIF under 30 dB SNR measurement noise, respectively. Under 10 dB SNR, mean CC is 0.37 for Tikhonov regularization, and mean CC ranges 0.53 to 0.78, and 0.53 to 0.98 for Bayesian MAP and MLIF, respectively.
Detecting Respiratory Effort-Related Arousals in Polysomnographic Data Using LSTM Networks

To diagnose sleep disorders, hours of sleep data from lots of different physiological sensors have to be analyzed. To do so, experts have to look through all the data which is time-consuming and error-prone. Automatic detection and classification of sleep related breathing disorders and arousals would significantly simplify this task. This years Physionet/CinC Challenge deals with this topic. This paper examines the use of a Long Short-Term Memory network for automatic arousal detection. On the test set, an AUPRC score of 0.14 was achieved.
A Multiscale Computational Model of Calcium-Mediated Ectopy in the Human Postinfarction Heart

A variety of arrhythmias following myocardial infarction are implicated with ectopic beats (EBs) resulting from spontaneous calcium (Ca2+) release (SCR) events. However, investigation of these arrhythmias is hampered by the lack of adequate techniques to assess with certainty abnormalities at the subcellular scale and arrhythmogenic events at the organ level. The aim of this study was to construct a multiscale computational model to investigate SCR-mediated ectopy within the infarcted human heart. To achieve this goal, an experimentally based phenomenological model of SCR events was integrated in the equations for Ca2+ cycling of an human ventricular action potential model. This augmented myocyte model was used in in-silico experiments with a postinfarction biventricular (BiV) model constructed based on magnetic resonance imaging data. The infarct scar and border zone (BZ) were segmented by thresholding the voxel intensity within the ventricular wall. These were then used to build a finite element mesh. Within the multiscale human BiV model we show that SCR-mediated DADs in cells interspersed with fibrosis in the infarcted BZ are capable of overcoming local source-sink mismatches to trigger an EB. The results presented here are the first to show that EBs resulting from abnormalities at the subcellular level can be studied using highly detailed human heart models.
Pilot Study on Electrocardiogram Derived Respiratory Rate Using a Wearable Armband

A pilot study on deriving respiratory rate from electrocardiogram (ECG) signals recorded by a self-developed wearable armband is presented. The armband includes a pair of dry electrodes which record ECG and it is designed for long-term monitoring. Armband-ECG and plethysmography-respiration signals were simultaneously recorded from 5 subjects (3 male) while paced breathing at constant rates from 0.1 to 0.4 Hz (with an increment of 0.1 Hz). Respiratory rate was estimated from the armband-ECG by using a method based on the variations in QRS slopes and R-wave angle. The estimations were compared to those obtained from the respiration signal. Obtained median and interquartile ranges of the relative error were lower than 4% for every requested respiratory rate. This suggests that normal ranges of spontaneous respiratory rate could be estimated from the wearable armband, allowing us to consider it for long-term wearable cardio and/or respiratory monitoring.
Microgravity Exposure Alters Sympathetic Modulation of Ventricular Repolarization Quantified From the ECG via Periodic Repolarization Dynamics

Prolonged microgravity exposure induces cardiovascular deconditioning, including orthostatic intolerance due to dysregulation of autonomic modulation of the cardiovascular system. Recent studies suggest that sympathetic modulation of ventricular repolarization can be assessed by measuring the index of Periodic Repolarization Dynamics (PRD), which quantifies low-frequency oscillations in ECG T-waves. In this study PRD was analyzed in ECGs from 22 volunteers at rest and during orthostatic Tilt-Table Test (TTT) performed before and after 60-day Head-Down (-6°) Bed Rest (HDBR) experiments to simulate microgravity effects. A very notable increase was found in resting PRD values measured at POST-HDBR with respect to PRE-HDBR: 2.70[3.21] deg2 vs 2.05[1.18] deg2 (median[IQR]). When PRD was evaluated during TTT, an even more remarkable increase was found in POST-HDBR with respect to PRE-HDBR: 4.25[6.46] deg2 vs 2.39[3.32] deg2. A jump-based countermeasure was only able to counteract microgravity-induced effects in response to TTT but not at baseline. In conclusion, prolonged exposure to simulated microgravity induces changes in ventricular re-polarization that are measurable by PRD and are more manifested when assessed following sympathetic provocation. A jump-based countermeasure is only partially effective in counteracting such effects.
Early Results on the Utilisation of ECG-Imaging During Catheter Ablation Procedures for Prediction of Sites of Earliest Activation During Re-entrant Ventricular Tachycardia

Success rate of ventricular tachycardia (VT) ablation remains sub-optimal. Current technology does not allow fast and accurate delineation of the ablation target. Noninvasive panoramic ECG-imaging (ECGI) offers the possibility of studying the interaction between arrhythmogenic substrate and earliest sites of activation during VT to improve ablation strategies. ECGI mapping (CardioInsight, Medtronic) was performed in 5 patients undergoing VT ablation. Ventricular pacing was delivered from the RV and three indices were measured at each ventricular site to map susceptibility to arrhythmia initiation: Re-entry vulnerability index (RVI), local dispersion of AT (ΔAT) and local dispersion of repolarization (ΔARI). Regions of high susceptibility were defined as those corresponding to the bottom 5% of RV I and the upper 5% of ΔAT and ΔARI. Morphologically distinct VTs were analyzed to measure the AT sequence and localize the region of earliest epicardial activation (AT < 5 ms). In total, 20 VTs were analyzed (4.0 ± 1.2 per patient). The minimum distance between the region of high vulnerability and the region of earliest AT during VT was 5.6 ± 8.6 mm for RV I, 6.1 ± 10.8 mm for ΔAT and 12.8 ± 22.4 mm for ΔARI (P > 0.13 for all pair-wise comparison). The vulnerable region presented at least partial overlap with the region of earliest activation during VT in 50%, 55% and 50% of all VTs for RV I, ΔAT and ΔARI, respectively. These early data confirm the mechanistic link between markers of arrhythmogenic risk and VT initiation and suggest that ECGI could be potentially used for targeting ablation in non-inducible or hemodynamically non-tolerated VTs.