Arrhythmias

Artificial intelligence-derived electrocardiographic age gap as a predictor of mortality after coronary revascularization: prognostic value and short-term intra-patient variability.

AIMS: The artificial intelligence (AI)-derived electrocardiographic (ECG) age gap-the difference between AI-predicted ECG age and chronological age-is an emerging biomarker of biological ageing linked to mortality. This study assessed its prognostic value for short- and long-term mortality after coronary artery bypass grafting (CABG) or percutaneous coronary intervention (PCI), addressing model bi...

Deep Learning-Based ROSC Prediction and ECG Phenotyping in Out-of-Hospital Cardiac Arrest.

INTRODUCTION: Electrocardiogram (ECG) signals during cardiac arrest contain detailed information on cardiac rhythm characteristics and have been associated with resuscitation outcomes. We aimed to develop a convolutional neural network (CNN) model to predict return of spontaneous circulation (ROSC) and identify novel ECG phenotypes in patients with out-of-hospital cardiac arrest (OHCA). METHODS: T...

NeuroCardioSense (NCS): A Time-Aware Fuzzy Decision Framework for Multi-Lead ECG Classification and Arrhythmia Detection.

Accurate classification of electrocardiogram (ECG) signals is essential for automated arrhythmia detection and clinical decision support. Existing dee...

3D ECG: a new simplified view.

BACKGROUND: The standard 12‑lead electrocardiogram (ECG) represents cardiac electrical activity through two-dimensional projections, requiring clinici...

Artificial Intelligence-Enabled Electrocardiographic Detection of Severe Aortic Stenosis Leading to Transcatheter Aortic Valve Replacement.

BACKGROUND: EchoNext is an artificial intelligence (artificial intelligence)-enabled electrocardiographic (ECG) model validated to detect unrecognized...

Deep learning-enabled ECG system for detecting left ventricular hypertrophy and predicting cardiovascular prognoses.

Left ventricular hypertrophy (LVH) is a common condition with a prevalence of 15%-20% in general population. Prior studies have suggested that deep le...

Validation of AI-enhanced ECG image analysis for identifying extreme cardiac magnetic resonance metrics in a cross-ethnic UK biobank study.

Artificial intelligence enhanced electrocardiography (AI-ECG) has shown promise in detecting cardiac abnormalities, but validation against cardiac mag...

Early electrocardiographic repolarization changes are associated with subclinical cancer therapy-related cardiac dysfunction in lymphoma patients: A machine learning-assisted longitudinal study.

UNLABELLED: Anthracycline-induced cardiotoxicity remains a significant clinical challenge. We evaluated longitudinal electrocardiographic (ECG) repola...

Towards seamless myocardial infarction and cardiac rhythm detection: a deep learning approach with automated paper-electrocardiogram digitisation.

PURPOSE: In India, myocardial infarction (MI) is a significant cause of mortality related to cardiovascular diseases. Timely diagnosis is critical for...

Multimodal Wearable Sensor-Based Stress Detection: Machine Learning Pipeline with Systematic Feature Selection and Key Biomarker Insights.

The increasing awareness of stress-related health impacts has driven demand for accurate, non-invasive stress detection methods, particularly those le...

Toward robust automated cardiovascular arrhythmia detection using self-supervised learning and 1-dimensional vision transformers.

Cardiovascular diseases are the leading cause of death worldwide. With electrocardiogram (ECG) machines becoming more accessible, passive monitoring f...

Artificial intelligence-enabled electrocardiography to predict impending episodes of ventricular tachycardia in individuals under continuous heart rhythm monitoring.

This is a protocol for a Cochrane Review (prognosis). The objectives are as follows: To identify and evaluate AI-based prognostic models, in developme...

BioFPT: Biosignal Feature Pyramid Transformer for self-supervised representation learning from ECGsignals.

Electrocardiogram (ECG) analysis represents a promising field for deep learning applications in clinical diagnostics. However, practical use of curren...

Hybrid Residual Neural Network for Obstructive Sleep Apnea Detection Using ECG Scalogram.

The electrocardiogram (ECG) has emerged as a viable alternative to polysomnography (PSG) for the detection of obstructive sleep apnea (OSA). Given the...

Artificial Intelligence-Enhanced Electrocardiography for Predicting Paroxysmal Atrial Fibrillation From Sinus Rhythm: Impact of Data Integration Across Institutions and Devices.

BACKGROUND: Artificial intelligence (AI)-enhanced electrocardiography (ECG) has been developed to detect paroxysmal atrial fibrillation (AF) from sinu...

Quantitative Histological Insights Into Sudden Arrhythmic Death Syndrome: Findings From a Forensic Autopsy Cohort.

Sudden arrhythmic death syndrome (SADS) is a major cause of sudden cardiac death in young individuals, characterized by structurally normal hearts and...

Automated detection of Chagas disease from ECG signals using wavelet scattering transform and RUSBoost classifier.

Early diagnosis of Chagas disease plays a vital role in enabling timely treatment and reducing the likelihood of underlying severe cardiovascular comp...

Deep Learning Based Approach for Lossless ECG Compression.

OBJECTIVE: Tele-monitoring is a useful platform for remote monitoring of cardiac patients, where compression plays a significant role in reducing the ...

Evaluation of artificial intelligence-based electrocardiogram analysis tools in patients with hypertrophic cardiomyopathy.

AIMS: Artificial intelligence (AI)-based electrocardiogram (ECG) analysis tools have shown promise in detecting various cardiac conditions. However, t...

Two stage fine-tuned multimodal generative AI for automated ECG based cardiovascular report generation.

The increasing prevalence of cardiovascular diseases (CVDs) calls for innovative diagnostic solutions that are both accurate and scalable. ElectroCard...