Cardiac Electrical Activity Research Articles

Electrocardiographic Changes in Second and Third Trimester in Bangladesh: A Comparative Study

Background: During pregnancy, there are notable changes in cardiovascular function to meet the increasing needs of the developing baby. The objective of this study was to compare ECG in pregnant women during their second and third trimesters with a control group of non-pregnant women in Bangladesh. This study aims to offer valuable insights into the cardiovascular changes that happen during pregnancy. This study's findings are important for maternal health research and clinical practice in Bangladesh and beyond. Objective: The purpose of this study is to analyze and compare the ECG variations in women during the second and third trimesters of pregnancy. Methods: The study took place in the gynecology Department at CMH Saidpur, Bangladesh. This study was conducted from March 2023 to August 2023. During this period, 75 pregnant patients in their 2nd trimester and 75 patients in their 3rd trimester were chosen as cases from antenatal outdoor, while 75 non-pregnant women of the same age were selected as the control group. We use the purposive sampling method. Results: During pregnancy, there were notable changes in BMI, heart rate, and PR interval. BMI decreased in the second trimester and increased in the third trimester.22.67% were nulliparous and 77.33% were multiparous in 2nd trimester. In the third trimester, 26.67% were nulliparous and 73.33% multiparous. So, no significant difference in parity. Heart rate increased in both trimesters, while the PR interval shortened in both trimesters. No significant changes were observed in the durations of the QRS complex or QT interval. In our study, the QTc interval displayed a significant rise. ST depression, which may suggest decreased blood flow, was exclusively seen in pregnant women, particularly in the third trimester. Conclusion: We found no significant changes in age, but BMI, heart rate, and PR interval were significantly different from the control group throughout pregnancy. No significant QRS complex or QT interval duration changes were identified. The QTc interval rose. ST depression, more prominent in the third trimester, only affected pregnant women. As shown by ST depression, pregnancy may change cardiac function, electrical activity, and ischemia.

Characteristics associated with prevalent atrial fibrillation and risk profile for future atrial fibrillation among those in sinus rhythm in an elderly population in the ELSA Brasil Study

Abstract Background Atrial fibrillation (AF) is an arrhythmia that can lead to limiting symptoms and increases the risk of stroke and heart failure (HF). Purpose To evaluate the association of clinical, electrocardiographic, and echocardiographic parameters with prevalent AF or flutter (AFF), and the risk profile for future AFF according to Charge-AF and EHR scores in an elderly population. Methods The ELSA-Brasil is a prospective cohort study designed to investigate cardiovascular disease and diabetes in 15,105 men and women, civil servants from universities or research institutions in six cities of Brazil. We included all participants in ELSA-Brasil aged 60 and over whose diagnosis of AFF could be assessed and had echocardiography performed at the study's baseline. From the initial sample with participants ≥ 60 years (n = 3,263), 400 participants were excluded due to missing data about AFF, and 775 who did not have echocardiographic studies. Results There were 88 (4.2%) participants with AFF at baseline among the 2,088 (65 ± 4.1 years; 53% women) included in this analysis. Those with AFF were older (66.8 ± 4.4 vs 65 ± 4.0 y-o) and had a higher prevalence of HF (16% vs 3.1%), previous myocardial infarction (14.8% vs 3.8%), left bundle branch block (LBBB: 4.6% vs 0,9%), prolonged QT interval (7.9% vs 2.0%), supraventricular extrasystoles (4.6% vs 1.2%), and sinus bradycardia (11.3% vs 2.9%). AFF individuals also presented larger left atrial volume index - LA (33.3 mL/m2 versus 27 mL/m2), left ventricular end-diastolic diameter (4,63 ± 0,6 cm versus 4,47 ± 0,4 cm) and lower left ventricular ejection fraction (LVEF: 63.6 ± 9.1% vs 67.4 ± 6.4%) that non-AFF (all p-values <0.05). In multivariable analysis, prevalent HF, LBBB, larger LA, and lower LVEF were independently associated with the presence of AFF. Among those in sinus rhythm at study baseline, both risk models showed that most individuals in our sample were at low risk. In the Charge-AF, 63% of individuals were identified as presenting low risk (<2.5%), and 12% of the sample was categorized as high risk, with a risk of five-year AFF greater than 5%. Similarly, in the EHR, 67% of participants were classified as low risk, and 13% were considered high risk. Conclusion We found that AFF was present in 4.2% of the subset of older participants in a cohort composed of free-living individuals in Brazil. The presence of AFF was associated with HF history, altered cardiac electrical activity, LA dilation, and worse LV systolic function. Among those with sinus rhythm, 12-13% were considered at high risk of developing AFF (> 5% in five years) based on two clinical scores. Clinical surveillance and monitoring of electrocardiography and echocardiography parameters may help the early identification of risk factors for AFF.

Cardiac electrical and functional activity following an outdoor cold-water swimming event

AimsParticipation in outdoor cold-water swimming (OCWS) events combines endurance exercise and cold exposure. Concerns have emerged about the potential risk of acute adverse cardiac events during OCWS, particularly during endurance events. We analysed the effect of prolonged OCWS on cardiac function in trained athletes. MethodsThe swimming event consisted of laps over a 1000-m course, for up to 6 h, in water at 15 °C. Twenty participants (11 males, 47.3 ± 8.6 years old) were included. Core temperature (Tcore) was monitored using an ingestible temperature sensor during and up to 1 h after the swim. Body composition, blood pressure, electrocardiogram (ECG), and transthoracic echocardiography were assessed 1 day before the event and within the first hour upon completion of the swim. ResultsMean body mass index was 27.1 ± 5.1 kg/m2 and fat mass was 25.2 ± 9.1 %. Mean duration of swimming was 214 ± 115 min. Minimum Tcore was 35.6 ± 1.3 °C. A significant lengthening of the QT interval corrected (QTc) for heart rate was observed post-exercise (437.7 ± 27.7 vs. 457.2 ± 35.9 ms, p = 0.012), with 5 participants exhibiting post-exercise QTc >500ms. OCWS did not alter the biventricular systolic function and left ventricular relaxation. No correlation was observed between ΔQTc and ΔTcore. ConclusionOCWS seemed to acutely delay post-exercise cardiac repolarization without alteration of cardiac function in a healthy trained population. Additional investigations would be warranted to explore the clinical implications of QT lengthening and its relationship with autonomic nervous system regulation during OCWS.

Cardiac arrhythmias in fish induced by natural and anthropogenic changes in environmental conditions.

A regular heartbeat is essential for maintaining the homeostasis of the vertebrate body. However, environmental pollutants, oxygen deficiency and extreme temperatures can impair heart function in fish. In this Review, we provide an integrative view of the molecular origins of cardiac arrhythmias and their functional consequences, from the level of ion channels to cardiac electrical activity in living fish. First, we describe the current knowledge of the cardiac excitation-contraction coupling of fish, as the electrical activity of the heart and intracellular Ca2+ regulation act as a platform for cardiac arrhythmias. Then, we compile findings on cardiac arrhythmias in fish. Although fish can experience several types of cardiac arrhythmia under stressful conditions, the most typical arrhythmia in fish - both under heat stress and in the presence of toxic substances - is atrioventricular block, which is the inability of the action potential to progress from the atrium to the ventricle. Early and delayed afterdepolarizations are less common in fish hearts than in the hearts of endotherms, perhaps owing to the excitation-contraction coupling properties of the fish heart. In fish hearts, Ca2+-induced Ca2+ release from the sarcoplasmic reticulum plays a smaller role than Ca2+ influx through the sarcolemma. Environmental changes and ion channel toxins can induce arrhythmias in fish and weaken their tolerance to environmental stresses. Although different from endotherm hearts in many respects, fish hearts can serve as a translational model for studying human cardiac arrhythmias, especially for human neonates.

The mechanisms of effects of oil-derived polyaromatic hydrocarbons on cardiac electrical activity in navaga cod (Eleginus nawaga)

The intensive development of oil and gas industries in the Arctic threatens Arctic aquatic ecosystems. The toxic and primarily lethal cardiotoxic effects of oil in living organisms are believed to be associated with polyaromatic hydrocarbons (PAHs), and previous works revealed the electrophysiological mechanisms of action of individual oil-derived PAHs. However, the physiological effects of a complex PAHs mixture in oil water-soluble fraction (WSF) have not been previously studied. This study is focused on the effects of oil WSF on electrical activity and major ionic currents in the working myocardium of navaga (Eleginus nawaga), which is one of the most important commercial fish species in the Arctic. We found that 1% and 10% solutions of oil WSF cause a marked increase in the duration of action potentials (APs) in navaga cardiomyocytes. This effect appears to be due to the suppression of rapid delayed rectifying current IKr (IC50 about 3% in ventricular and atrial myocardium). At higher concentrations, oil WSF also suppressed calcium current ICaL (IC50 = 10.6%), which led to a decrease in the contractile activity in isolated myocardial preparations. Unlike individual tricyclic PAHs, oil WSF did not affect fast sodium current INa and AP upstroke velocity. An assessment of the content of tricyclic PAHs in 10% solution of oil WSF showed that their total concentration is relatively low and does not exceed 100 nM. Thus, oil WSF also has a powerful cardiotoxic effect in fish myocardium, but its effects differ from the previously studied effects of tricyclic PAHs and suggest the presence of yet unexplored oil compounds that have a more powerful toxic potential against ERG channels.

His bundle pacing in a patient with complete atrioventricular block and congenitally corrected transposition of the great arteries: potential of the use of extended reality and cardiac electrical activity projected into 3D heart model.

His bundle pacing in a patient with complete atrioventricular block and congenitally corrected transposition of the great arteries: potential of the use of extended reality and cardiac electrical activity projected into 3D heart model.

Fast interactive simulations of cardiac electrical activity in anatomically accurate heart structures by compressing sparse uniform cartesian grids

Background and Objective:Numerical simulations are valuable tools for studying cardiac arrhythmias. Not only do they complement experimental studies, but there is also an increasing expectation for their use in clinical applications to guide patient-specific procedures. However, numerical studies that solve the reaction–diffusion equations describing cardiac electrical activity remain challenging to set up, are time-consuming, and in many cases, are prohibitively computationally expensive for long studies. The computational cost of cardiac simulations of complex models on anatomically accurate structures necessitates parallel computing. Graphics processing units (GPUs), which have thousands of cores, have been introduced as a viable technology for carrying out fast cardiac simulations, sometimes including real-time interactivity. Our main objective is to increase the performance and accuracy of such GPU implementations while conserving computational resources. Methods:In this work, we present a compression algorithm that can be used to conserve GPU memory and improve efficiency by managing the sparsity that is inherent in using Cartesian grids to represent cardiac structures directly obtained from high-resolution MRI and mCT scans. Furthermore, we present a discretization scheme that includes the cross-diagonal terms in the computational cell to increase numerical accuracy, which is especially important for simulating thin tissue sections without the need for costly mesh refinement. Results:Interactive WebGL simulations of atrial/ventricular structures (on PCs, laptops, tablets, and phones) demonstrate the algorithm’s ability to reduce memory demand by an order of magnitude and achieve calculations up to 20x faster. We further showcase its superiority in slender tissues and validate results against experiments performed in live explanted human hearts. Conclusions:In this work, we present a compression algorithm that accelerates electrical activity simulations on realistic anatomies by an order of magnitude (up to 20x), thereby allowing the use of finer grid resolutions while conserving GPU memory. Additionally, improved accuracy is achieved through cross-diagonal terms, which are essential for thin tissues, often found in heart structures such as pectinate muscles and trabeculae, as well as Purkinje fibers. Our method enables interactive simulations with even interactive domain boundary manipulation (unlike finite element/volume methods). Finally, agreement with experiments and ease of mesh import into WebGL paves the way for virtual cohorts and digital twins, aiding arrhythmia analysis and personalized therapies.

Solution of the Forward Problem of ECG using Radial basis Function Network

ECG inverse problem is a mathematical formulation of cardiac electrical activity on the heart surface that can be solved to determine the electric potential on the heart surface. In this paper, a forward problem to ECG will be formulated using a mathematical formulation similar to the ECG forward problem. The forward problem will be solved using radial basis collocation techniques by taking different discretization points of N=9 and N=25 points to predict the solution u(x,t) to the forward problem.

Computational Modeling of Sodium-Ion-Channel-Based Glucose Sensing Biophysics to Study Cardiac Pacemaker Action Potential

Elevated blood glucose levels, known as hyperglycemia, play a significant role in sudden cardiac arrest, often resulting in sudden cardiac death, particularly among those with diabetes. Understanding the internal mechanisms has been a challenge for healthcare professionals, leading many research groups to investigate the relationship between blood glucose levels and cardiac electrical activity. Our hypothesis suggests that glucose-sensing biophysics mechanisms in cardiac tissue could clarify this connection. To explore this, we adapted a single-compartment computational model of the human pacemaker action potential. We incorporated glucose-sensing mechanisms with voltage-gated sodium ion channels using ordinary differential equations. Parameters for the model were based on existing experimental studies to mimic the impact of glucose levels on pacemaker action potential firing. Simulations using voltage clamp and current clamp techniques showed that elevated glucose levels decreased sodium ion channel currents, leading to a reduction in the pacemaker action potential frequency. In summary, our mathematical model provides a cellular-level understanding of how high glucose levels can lead to bradycardia and sudden cardiac death.

Using animations of cardiac electrical activity to improve medical students' understanding of cardiac pathology and ECG traces

Using animations of cardiac electrical activity to improve medical students' understanding of cardiac pathology and ECG traces

A New Remote Monitoring System: Evaluation of the Efficiency and Accuracy of the Smart Emergency Medical System-Health Internet of Things Device.

The remote medical monitoring system can facilitate monitoring patients with cardiac arrhythmia, and consequently, reduce mortality and complications in individuals requiring emergency interventions. Hence, it is necessary to evaluate new telemedicine devices and compare them with standard devices. Therefore, this study aimed to evaluate and compare the new remote monitoring system, Smart Emergency Medical System-Health Internet of Things (SEMS-HIOT) developed by the Health Technology Development Centre of Babol University of Medical Sciences on patients with different cardiac arrhythmias and compare it with the standard device. In this case-control study, 60 patients were divided into the six most common arrhythmia groups (n=10 per each group and equal gender) as atrial fibrillation, ventricular tachycardia, paroxysmal supraventricular tachycardia, premature ventricular contractions, atrial tachycardia, and premature atrial contractions. Also, 20 healthy individuals (including 10 men and 10 women) without any arrhythmia (normal rhythm) were considered as the control group. Three similar SEMS-HIOT devices were used as test devices and a standard cardiac monitoring device as the control device. The clinical parameters, including heart rate, pulse rate, oxygen saturation, body temperature, and cardiac electrical activity via electrocardiogram (ECG) lead-II were recorded. Findings showed that the performance of the SEMS-HIOT test device was similar and in the same range for all indices in each group and there were no significant differences compared to the performance of the control device (P0.05). Also, the ECG records measured with SEMS-HIOT and standard device indicate no significant differences (P0.05). Our study showed that the cardiac indices as well as ECG findings, which were measured with SEMS-HIOT and common standard devices confirmed the accuracy and reliability of the new telematics device for monitoring patients with cardiac diseases.

Potassium dependent structural changes in the selectivity filter of HERG potassium channels

The fine tuning of biological electrical signaling is mediated by variations in the rates of opening and closing of gates that control ion flux through different ion channels. Human ether-a-go-go related gene (HERG) potassium channels have uniquely rapid inactivation kinetics which are critical to the role they play in regulating cardiac electrical activity. Here, we exploit the K+ sensitivity of HERG inactivation to determine structures of both a conductive and non-conductive selectivity filter structure of HERG. The conductive state has a canonical cylindrical shaped selectivity filter. The non-conductive state is characterized by flipping of the selectivity filter valine backbone carbonyls to point away from the central axis. The side chain of S620 on the pore helix plays a central role in this process, by coordinating distinct sets of interactions in the conductive, non-conductive, and transition states. Our model represents a distinct mechanism by which ion channels fine tune their activity and could explain the uniquely rapid inactivation kinetics of HERG.

Cardiovascular disease detection from cardiac arrhythmia ECG signals using artificial intelligence models with hyperparameters tuning methodologies

Cardiovascular disease (CVD) is connected with irregular cardiac electrical activity, which can be seen in ECG alterations. Due to its convenience and non-invasive aspect, the ECG is routinely exploited to identify different arrhythmias and automatic ECG recognition is needed immediately. In this paper, enhancement for the detection of CVDs such as Ventricular Tachycardia (VT), Premature Ventricular Contraction (PVC) and ST Change (ST) arrhythmia using different dimensionality reduction techniques and multiple classifiers are presented. Three-dimensionality reduction methods, such as Local Linear Embedding (LLE), Diffusion Maps (DM), and Laplacian Eigen (LE), are employed. The dimensionally reduced ECG samples are further feature selected with Cuckoo Search (CS) and Harmonic Search Optimization (HSO) algorithms. A publicly available MIT-BIH (Physionet) - VT database, PVC database, ST Change database and NSR database were used in this work. The cardiac vascular disturbances are classified by using seven classifiers such as Gaussian Mixture Model (GMM), Expectation Maximization (EM), Non-linear Regression (NLR), Logistic Regression (LR), Bayesian Linear Discriminant Analysis (BDLC), Detrended Fluctuation Analysis (Detrended FA), and Firefly. For different classes, the average overall accuracy of the classification techniques is 55.65 % when without CS and HSO feature selection, 64.36 % when CS feature selection is used, and 75.39 % when HSO feature selection is used. Also, to improve the performance of classifiers, the hyperparameters of four classifiers (GMM, EM, BDLC and Firefly) are tuned with the Adam and Grid Search Optimization (GSO) approaches. The average accuracy of classification for the CS feature-based classifiers that used GSO and Adam hyperparameter tuning was 79.92 % and 85.78 %, respectively. The average accuracy of classification for the HSO feature-based classifiers that used GSO and Adam hyperparameter tuning was 86.87 % and 93.77 %, respectively. The performance of the classifier is analyzed based on the accuracy parameter for both with and without feature selection methods and with hyperparameter tuning techniques. In the case of ST vs. NSR, a higher accuracy of 98.92 % is achieved for the LLE dimensionality reduction with HSO feature selection for the GMM classifier with Adam's hyperparameter tuning approach. The GMM classifier with the Adam hyperparameter tuning approach with 98.92 % accuracy in detecting ST vs. NSR cardiac disease is outperforming all other classifiers and methodologies.

Three-Dimensional Electrodes of Liquid Metals for Long-Term, Wireless Cardiac Analysis and Modulation.

Cardiovascular disease is a major public health issue, and smart diagnostic approaches play an important role in the analysis of electrocardiograms. Here, we present three-dimensional, soft electrodes of liquid metals that can be conformably attached to the surfaces of the heart and skin for long-term cardiac analysis. The fine micropillar structures of biocompatible liquid metals enable precise targeting to small tissue areas, allowing for spatiotemporal mapping and modulation of cardiac electrical activity with high resolution. The low mechanical modulus of these liquid-metal electrodes not only helps avoid inflammatory responses triggered by modulus mismatch between the tissue and electrodes, but also minimizes pain when embedded in biological tissues such as the skin and heart. Furthermore, in vivo experiments with animal models and human trials demonstrate long-term and accurate monitoring of electrocardiograms over a period of 30 days.

Advancing cardiac diagnostics: Exceptional accuracy in abnormal ECG signal classification with cascading deep learning and explainability analysis

Arrhythmias, cardiac rhythm disorders, demand precise diagnosis for effective treatment planning, emphasizing the crucial role of electrocardiogram (ECG) signal interpretation. While deep learning excels in ECG classification, its interpretability remains a challenge. Addressing this issue, our research introduces an innovative approach employing image-based ECG representations and cascading deep neural networks (CDNNs) to enhance arrhythmia detection precision. Initiating with the transformation of 12-lead ECG time-series data, particularly focusing on Lead II, into image-based representations using relative positioning matrices (RPM), this novel conversion captures spatial and temporal information concurrently, enriching the depth of our data analysis. Subsequently, CDNNs play a pivotal role in feature extraction, designed to automatically extract impactful features from image-based ECG representations, providing profound insights into cardiac electrical activity. Extracted features serve as inputs for the subsequent deep neural network for classification. Experimental results on the Shaoxing–Chapman ECG database, encompassing over 10,000 recordings with an 80/10/10 split, 80/20 split, 60/40 split, and 10-fold cross-validation, showcase a remarkable 100% accuracy in distinguishing between seven and four different arrhythmia classes. Incorporating SHapley Additive exPlanations (SHAP) values, our methodology offers comprehensive insights into the model’s decision-making, ensuring interpretability. Additionally, Gradient-weighted Class Activation Mapping (Grad-CAM) aids feature visualization, elucidating key features driving predictions. By amalgamating image-based representation, CDNNs, and interpretability techniques, our research not only attains exceptional classification performance but also boosts model transparency and trustworthiness. These findings hold promise for advancing arrhythmia diagnosis and enhancing interpretability in deep learning-based medical applications.

Study on ECG Signal Classification and Athlete Health Analysis Based on Attention Mechanism

Abstract In the training and competition process of athletes, their bodies are subjected to various levels of load and stress. As an important diagnostic tool, ECG signals can provide deep insights into the cardiac function of athletes, including heart rate, rhythm, and changes in cardiac electrical activity. By conducting a thorough examination of ECG readings, we are able to quickly identify possible heart conditions or irregularities, which is essential for preserving the heart health of athletes. However, ECG signals are highly complex and multidimensional. To accurately classify these signals, it is necessary to select the most representative and discriminative features. However, this is not an easy task, and the selection of effective features remains a pressing issue. To address this problem, this paper proposes the CSNet classification network model. This framework eradicates disruptions in electrocardiogram signals, performs attribute extraction via a direct network configuration, and combines channel focus mechanisms and spatial focus mechanisms to enhance attribute representation and categorization capabilities. Furthermore, to retain the temporal information of ECG signals, we introduce the Gated Recurrent Unit (GRU), which helps to better capture temporal patterns and dependencies in the signals, thus enabling more accurate classification of ECG signals.

Hybrid Neural State-Space Modeling for Supervised and Unsupervised Electrocardiographic Imaging.

State-space modeling (SSM) provides a general framework for many image reconstruction tasks. Error in a priori physiological knowledge of the imaging physics, can bring incorrectness to solutions. Modern deep-learning approaches show great promise but lack interpretability and rely on large amounts of labeled data. In this paper, we present a novel hybrid SSM framework for electrocardiographic imaging (ECGI) to leverage the advantage of state-space formulations in data-driven learning. We first leverage the physics-based forward operator to supervise the learning. We then introduce neural modeling of the transition function and the associated Bayesian filtering strategy. We applied the hybrid SSM framework to reconstruct electrical activity on the heart surface from body-surface potentials. In unsupervised settings of both in-silico and in-vivo data without cardiac electrical activity as the ground truth to supervise the learning, we demonstrated improved ECGI performances of the hybrid SSM framework trained from a small number of ECG observations in comparison to the fixed SSM. We further demonstrated that, when in-silico simulation data becomes available, mixed supervised and unsupervised training of the hybrid SSM achieved a further 40.6% and 45.6% improvements, respectively, in comparison to traditional ECGI baselines and supervised data-driven ECGI baselines for localizing the origin of ventricular activations in real data.

Development of a novel four-channel atomic gradiometric magnetometer for magnetocardiography: Advancing non-invasive cardiac research with enhanced sensitivity and spatial resolution

The field of magnetocardiography (MCG) has witnessed significant advancements with the use of atomic magnetometers (AMs) and gradiometers, offering numerous advantages over traditional superconducting quantum interference devices (SQUIDs). This study focuses on the development of a highly sensitive four-channel atomic gradiometer specifically designed for measuring the biomagnetic fields of a rat’s heart. By utilizing gradiometric detection and monitoring deviations in the Larmor frequency of rubidium (Rb) atoms, this gradiometer captures cardiac signals with exceptional sensitivity and spatial resolution. One of the key challenges in utilizing AMs as gradiometers lies in minimizing cross-talk effects among the sensors to ensure accurate measurements. In this study, we successfully address this challenge, leading to precise and reliable data acquisition. Furthermore, our gradiometer demonstrates a linear response across a wide range of frequencies, enhancing its versatility and applicability in various experimental setups. The achieved sensitivity of 350 fT/√Hz by our atomic gradiometer showcases its potential in assessing MCG measurements for studying cardiac electrical activities. The non-invasive nature of the technique, combined with the elimination of cryogenic cooling requirements, opens up new avenues for cardiac research and diagnostics. The enhanced spatial and spatiotemporal resolution offered by optically pumped magnetometers (OPMs) further enhances our ability to understand and analyze complex cardiac phenomena.

Unsupervised stochastic learning and reduced order modeling for global sensitivity analysis in cardiac electrophysiology models

Background and Objective:Numerical simulations in electrocardiology are often affected by various uncertainties inherited from the lack of precise knowledge regarding input values including those related to the cardiac cell model, domain geometry, and boundary or initial conditions used in the mathematical modeling. Conventional techniques for uncertainty quantification in modeling electrical activities of the heart encounter significant challenges, primarily due to the high computational costs associated with fine temporal and spatial scales. Additionally, the need for numerous model evaluations to quantify ubiquitous uncertainties increases the computational challenges even further. Methods:In the present study, we propose a non-intrusive surrogate model to perform uncertainty quantification and global sensitivity analysis in cardiac electrophysiology models. The proposed method combines an unsupervised machine learning technique with the polynomial chaos expansion to reconstruct a surrogate model for the propagation and quantification of uncertainties in the electrical activity of the heart. The proposed methodology not only accurately quantifies uncertainties at a very low computational cost but more importantly, it captures the targeted quantity of interest as either the whole spatial field or the whole temporal period. In order to perform sensitivity analysis, aggregated Sobol indices are estimated directly from the spectral mode of the polynomial chaos expansion. Results:We conduct Uncertainty Quantification (UQ) and global Sensitivity Analysis (SA) considering both spatial and temporal variations, rather than limiting the analysis to specific Quantities of Interest (QoIs). To assess the comprehensive performance of our methodology in simulating cardiac electrical activity, we utilize the monodomain model. Additionally, sensitivity analysis is performed on the parameters of the Mitchell-Schaeffer cell model. Conclusions:Unlike conventional techniques for uncertainty quantification in modeling electrical activities, the proposed methodology performs at a low computational cost the sensitivity analysis on the cardiac electrical activity parameters. The results are fully reproducible and easily accessible, while the proposed reduced-order model represents a significant contribution to enhancing global sensitivity analysis in cardiac electrophysiology.

A critically prolonged avalanche burial with recorded cardiac electrical activity and complete recovery - a case report

The probability of survival in avalanche accidents is time-dependent. Critically buried victims who undergo a long burial duration (over 60 min) face a possible mortality rate of over 80%. Understanding the physiological response during critical avalanche burial is crucial for improving rescue strategies and outcomes. We present the case of a 55-year-old male skier buried under an avalanche for 4 h and 51 min in the Italian Alps. Continuous heart rate monitoring revealed distinct phases of cardiac activity during burial. Despite severe hypothermia, the victim survived without extracorporeal rewarming. This case highlights the importance of continuous monitoring and appropriate on-site management in avalanche accidents. Factors such as the presence of an air pocket may positively influence survival. This case underscores the importance of comprehensive resuscitative measures and guidelines for managing avalanche victims with prolonged burial durations.