Arrhythmias

Predicting Mortality in Atrial Fibrillation Patients Treated with Direct Oral Anticoagulants: A Machine Learning Study Based on the MIMIC-IV Database.

Atrial fibrillation (AF) is a common arrhythmia linked to increased mortality and significant healthcare burden, especially in the elderly. Direct Oral Anticoagulants (DOACs) are crucial for stroke prevention in AF, offering benefits over traditional vitamin K antagonists. Despite scoring systems like HATCH and CHA2DS2-VASc, their predictive ability for mortality in AF patients is limited. This s...

Development of a Heart Rate Variability Based Ambulatory Stress Detection Model for Clinical Populations.

Biosensor-based, real-time stress detection has generated clinical interest for the purpose of driving just-in-time interventions that support recovery from mental disorders. Most stress detection models to date, however, have been trained with laboratory-based data from homogenous samples of healthy adults, and do not perform as well in clinical populations. As an initial step toward the developm...

A Deep Learning-Based Multimodal Fusion Model for Recurrence Prediction in Persistent Atrial Fibrillation Patients.

BACKGROUND: The long-term success rate of atrial fibrillation (AF) ablation remains a significant clinical challenge, particularly in patients with pe...

Real-Time Implementation of Accelerated HCP-MMA for Deep Learning-Based ECG Arrhythmia Classification Using Contour-Based Visualization.

This study presents a real-time implementation of an accelerated Hurst Contour Projection from Multiscale Multifractal Analysis (HCP-MMA) for deep lea...

Induced Pluripotent Stem Cells in Cardiomyopathy: Advancing Disease Modeling, Therapeutic Development, and Regenerative Therapy.

Cardiomyopathies are a heterogeneous group of heart muscle diseases that can lead to heart failure, arrhythmias, and sudden cardiac death. Traditional...

Multi-branch GAT-GRU-transformer for explainable EEG-based finger motor imagery classification.

Electroencephalography (EEG) provides a non-invasive and real-time approach to decoding motor imagery (MI) tasks, such as finger movements, offering s...

Atrial fibrillation detection via contactless radio monitoring and knowledge transfer.

Atrial fibrillation (AF) has been a prevalent and serious arrhythmia associated with increased morbidity and mortality worldwide. The Electrocardiogra...

Artificial intelligence and the electrocardiogram: A modern renaissance.

Integrating Artificial Intelligence (AI) with electrocardiograms (ECG) represents a transformative shift in cardiovascular medicine, marking a modern ...

A Talk with ChatGPT: The Role of Artificial Intelligence in Shaping the Future of Cardiology and Electrophysiology.

: Artificial intelligence (AI) is poised to significantly impact the future of cardiology and electrophysiology, offering new tools to interpret compl...

A novel method of BiFormer with temporal-spatial characteristics for ECG-based PVC detection.

INTRODUCTION: Premature Ventricular Contractions (PVCs) can be warning signs for serious cardiac conditions, and early detection is essential for prev...

ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning.

Deep learning-based electrocardiogram (ECG) classification has shown impressive performance but clinical adoption has been slowed by the lack of trans...

Unsupervised Clustering of Patients Undergoing Thoracoscopic Ablation Identifies Relevant Phenotypes for Advanced Atrial Fibrillation.

: The rate of recurrence after ablation for atrial fibrillation (AF) is considerable. Risk stratification for AF recurrence after ablation remains inc...

Robust performances of a nocturnal long-term ECG algorithm for the evaluation of sleep apnea syndrome: A pilot study.

Obstructive sleep apnea-hypopnea syndrome (OSAHS) is one of the most common sleep disorders affecting nearly one billion of the global adult populatio...

Utility of convolutional neural network-enhanced electrocardiogram to diagnose and predict mitral regurgitation in patients with chronic atrial fibrillation.

A convolutional neural network (CNN)-enhanced electrocardiogram (ECG) has been reported for detecting mitral regurgitation (MR). This tool may be part...

Enhancing ECG Classification in Cardiac Diagnostics: A Novel Approach Using Adaptive Focal Cross-Entropy Loss Function.

Heart disease is the leading cause of mortality globally. Electrocardiograms (ECGs) are standard instruments for the examination of heart conditions, ...

Role of Mobile Health and Wearable Devices in Cardiovascular Disease Prevention: A Comprehensive Review.

Cardiovascular disease (CVD) is a leading cause of death worldwide. A key area of interest in CVD prevention is novel digital health technologies, pri...

Machine learning models for predicting survival in lung cancer patients undergoing microwave ablation.

OBJECTIVE: To develop and validate predictive models assessing survival outcomes in patients with non-small cell lung cancer (NSCLC) treated with micr...

A Review on Intelligent Systems for ECG Analysis: From Flexible Sensing Technology to Machine Learning.

This paper conducts an extensive review of flexible cardiac sensing devices designed for electrocardiogram (ECG) acquisitions, with emphasis on their ...

TCGAN: Temporal Convolutional Generative Adversarial Network for Fetal ECG Extraction Using Single-Channel Abdominal ECG.

Noninvasive fetal ECG (FECG) monitoring holds significant importance in ensuring the normal development of the fetus. Since FECG is usually submerged ...

Integrating anatomy and electrophysiology in the healthy human heart: Insights from biventricular statistical shape analysis using universal coordinates.

A cardiac digital twin is a virtual replica of a patient-specific heart, mimicking its anatomy and physiology. A crucial step of building a cardiac di...