Statistical model for describing heart rate variability in normal rhythm and atrial fibrillation

• URL: http://arxiv.org/abs/2207.08165v1
• Date: Sun, 17 Jul 2022 13:06:39 GMT
• Title: Statistical model for describing heart rate variability in normal rhythm and atrial fibrillation
• Abstract summary: Heart rate variability (HRV) indices describe properties of interbeat intervals in electrocardiogram (ECG) Atrial fibrillation (AF) is the most widespread cardiac arrhythmia in human population.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Heart rate variability (HRV) indices describe properties of interbeat intervals in electrocardiogram (ECG). Usually HRV is measured exclusively in normal sinus rhythm (NSR) excluding any form of paroxysmal rhythm. Atrial fibrillation (AF) is the most widespread cardiac arrhythmia in human population. Usually such abnormal rhythm is not analyzed and assumed to be chaotic and unpredictable. Nonetheless, ranges of HRV indices differ between patients with AF, yet physiological characteristics which influence them are poorly understood. In this study, we propose a statistical model that describes relationship between HRV indices in NSR and AF. The model is based on Mahalanobis distance, the k-Nearest neighbour approach and multivariate normal distribution framework. Verification of the method was performed using 10 min intervals of NSR and AF that were extracted from long-term Holter ECGs. For validation we used Bhattacharyya distance and Kolmogorov-Smirnov 2-sample test in a k-fold procedure. The model is able to predict at least 7 HRV indices with high precision.

Related papers

• A Latent ODE Approach to Spatiotemporal Modeling of Cine Cardiac MRI [35.84205082882139]
  We present a latent dynamical model that encodes bi-ventricular anatomy and full-cycle cine motion as a continuous trajectory.<n>We studied 72,386 UK Biobank participants without baseline cardiovascular disease, including 367 incident heart failure events.
  arXiv  Detail & Related papers  (2026-06-25T07:53:45Z)
• Motion-Robust Multimodal Fusion of PPG and Accelerometer Signals for Three-Class Heart Rhythm Classification [5.573626931805866]
  Atrial fibrillation (AF) is a leading cause of stroke and mortality, particularly in elderly patients.<n>We introduce RhythmiNet, a residual neural network enhanced with temporal and channel attention modules.<n>The model performs three-class rhythm classification: AF, sinus rhythm (SR) and Other.
  arXiv  Detail & Related papers  (2025-11-02T14:21:55Z)
• Detection of Autonomic Dysreflexia in Individuals With Spinal Cord Injury Using Multimodal Wearable Sensors [2.208475400165877]
  Autonomic Dysreflexia (AD) is a potentially life-threatening condition characterized by sudden, severe blood pressure spikes in individuals with spinal cord injury (SCI)<n>This study presents a non-invasive, explainable machine learning framework for detecting AD using multimodal wearable sensors.
  arXiv  Detail & Related papers  (2025-07-23T21:18:23Z)
• Acoustic Index: A Novel AI-Driven Parameter for Cardiac Disease Risk Stratification Using Echocardiography [0.0]
  We introduce the Acoustic Index, a novel AI-derived echocardiographic parameter designed to quantify cardiac dysfunction from standard ultrasound views.<n>The model combines Extended Dynamic Mode Decomposition (EDMD) based on Koopman operator theory with a hybrid neural network that incorporates clinical metadata.<n>In a prospective cohort of 736 patients, encompassing various cardiac pathologies and normal controls, the Acoustic Index achieved an area under the curve (AUC) of 0.89 in an independent test set.<n>Cross-validation across five folds confirmed the robustness of the model, showing that both sensitivity and specificity exceeded 0.8 when evaluated on independent data.
  arXiv  Detail & Related papers  (2025-07-17T21:27:28Z)
• Global and Local Contrastive Learning for Joint Representations from Cardiac MRI and ECG [40.407824759778784]
  PTACL (Patient and Temporal Alignment Contrastive Learning) is a multimodal contrastive learning framework that enhances ECG representations by integrating-temporal information from CMR.<n>We evaluate PTACL on paired ECG-CMR data from 27,951 subjects in the UK Biobank.<n>Our results highlight the potential of PTACL to enhance non-invasive cardiac diagnostics using ECG.
  arXiv  Detail & Related papers  (2025-06-24T17:19:39Z)
• Leveraging Cardiovascular Simulations for In-Vivo Prediction of Cardiac Biomarkers [43.17768785084301]
  We train an amortized neural posterior estimator on a newly built large dataset of cardiac simulations. We incorporate elements modeling effects to better align simulated data with real-world measurements. The proposed framework can further integrate in-vivo data sources to refine its predictive capabilities on real-world data.
  arXiv  Detail & Related papers  (2024-12-23T13:05:17Z)
• Electromechanical Dynamics of the Heart: A Study of Cardiac Hysteresis During Physical Stress Test [6.353221273197041]
  We investigated electromechanical intervals (RR, QT, systolic, and diastolic) and their interactions during exercise. Deep learning models estimated the QT, systolic, and diastolic intervals from RR data, confirming the nonlinear relationship between RR and other intervals. Findings highlight a significant cardiac memory effect, linking ECG and PCG morphology and timing to heart rate history.
  arXiv  Detail & Related papers  (2024-10-25T16:23:19Z)
• Self-supervised inter-intra period-aware ECG representation learning for detecting atrial fibrillation [41.82319894067087]
  We propose an inter-intra period-aware ECG representation learning approach. Considering ECGs of atrial fibrillation patients exhibit the irregularity in RR intervals and the absence of P-waves, we develop specific pre-training tasks for interperiod and intraperiod representations. Our approach demonstrates remarkable AUC performances on the BTCH dataset, textiti.e., 0.953/0.996 for paroxysmal/persistent atrial fibrillation detection.
  arXiv  Detail & Related papers  (2024-10-08T10:03:52Z)
• Learning Cortico-Muscular Dependence through Orthonormal Decomposition of Density Ratios [39.3721526159124]
  We present a novel application of statistical dependence estimators based on orthonormal decomposition of density ratios to model the relationship between cortical and muscle oscillations.<n>We experimentally demonstrate that eigenfunctions learned from cortico-muscular connectivity can accurately classify movements and subjects.
  arXiv  Detail & Related papers  (2024-10-04T16:05:08Z)
• Model-driven Heart Rate Estimation and Heart Murmur Detection based on Phonocardiogram [4.5546756241897235]
  This study utilizes a publicly available phonocardiogram (PCG) dataset to estimate heart rate. We extend the best-performing model to a multi-task learning framework for simultaneous heart rate estimation and murmur detection.
  arXiv  Detail & Related papers  (2024-07-25T22:56:21Z)
• Improving Diffusion Models for ECG Imputation with an Augmented Template Prior [43.6099225257178]
  noisy and poor-quality recordings are a major issue for signals collected using mobile health systems. Recent studies have explored the imputation of missing values in ECG with probabilistic time-series models. We present a template-guided denoising diffusion probabilistic model (DDPM), PulseDiff, which is conditioned on an informative prior for a range of health conditions.
  arXiv  Detail & Related papers  (2023-10-24T11:34:15Z)
• Three-dimensional micro-structurally informed in silico myocardium -- towards virtual imaging trials in cardiac diffusion weighted MRI [58.484353709077034]
  We propose a novel method to generate a realistic numerical phantom of myocardial microstructure. In-silico tissue models enable evaluating quantitative models of magnetic resonance imaging.
  arXiv  Detail & Related papers  (2022-08-22T22:01:44Z)
• Global ECG Classification by Self-Operational Neural Networks with Feature Injection [25.15075119957447]
  We propose a novel approach for inter-patient ECG classification using a compact 1D Self-Organized Operational Neural Networks (Self-ONNs) We used 1D Self-ONN layers to automatically learn morphological representations from ECG data, enabling us to capture the shape of the ECG waveform around the R peaks. Using the MIT-BIH arrhythmia benchmark database, the proposed method achieves the highest classification performance ever achieved.
  arXiv  Detail & Related papers  (2022-04-07T22:49:18Z)
• Estimation of atrial fibrillation from lead-I ECGs: Comparison with cardiologists and machine learning model (CurAlive), a clinical validation study [0.0]
  This study presents a method to detect atrial fibrillation with lead-I ECGs using artificial intelligence. The aim of the study is to compare the accuracy of the diagnoses estimated by cardiologists and artificial intelligence over lead-I ECGs.
  arXiv  Detail & Related papers  (2021-04-15T12:50:16Z)
• Spatio-temporal Multi-task Learning for Cardiac MRI Left Ventricle Quantification [6.887389908965403]
  We propose a learning-temporal multi-task approach to obtain a complete set of measurements of cardiac left ventricle (LV) morphology. We first segment LVs using an encoder-decoder network and then introduce a framework to regress 11 LV indices and classify the cardiac phase. The proposed model is based on the 3D-temporal convolutions, which extract spatial and features from MR images.
  arXiv  Detail & Related papers  (2020-12-24T17:48:35Z)
• Identification of Ischemic Heart Disease by using machine learning technique based on parameters measuring Heart Rate Variability [50.591267188664666]
  In this study, 18 non-invasive features (age, gender, left ventricular ejection fraction and 15 obtained from HRV) of 243 subjects were used to train and validate a series of several ANN. The best result was obtained using 7 input parameters and 7 hidden nodes with an accuracy of 98.9% and 82% for the training and validation dataset.
  arXiv  Detail & Related papers  (2020-10-29T19:14:41Z)
• Prediction of the onset of cardiovascular diseases from electronic health records using multi-task gated recurrent units [51.14334174570822]
  We propose a multi-task recurrent neural network with attention mechanism for predicting cardiovascular events from electronic health records. The proposed approach is compared to a standard clinical risk predictor (QRISK) and machine learning alternatives using 5-year data from a NHS Foundation Trust.
  arXiv  Detail & Related papers  (2020-07-16T17:43:13Z)
• Segmentation of the Myocardium on Late-Gadolinium Enhanced MRI based on 2.5 D Residual Squeeze and Excitation Deep Learning Model [55.09533240649176]
  The aim of this work is to develop an accurate automatic segmentation method based on deep learning models for the myocardial borders on LGE-MRI. A total number of 320 exams (with a mean number of 6 slices per exam) were used for training and 28 exams used for testing. The performance analysis of the proposed ensemble model in the basal and middle slices was similar as compared to intra-observer study and slightly lower at apical slices.
  arXiv  Detail & Related papers  (2020-05-27T20:44:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.