Cardiovascular

Arrhythmias

Latest AI and machine learning research in arrhythmias for healthcare professionals.

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Showing 1912-1932 of 2,496 articles
Deep learning predicts cardiac output from seismocardiographic signals in heart failure

Determination of cardiac output (CO) is essential to the clinical management of cardiovascular compr...

Personalized Synthetic Electrocardiograms with Outcomes

Synthetic data can be the solution to privacy requirements, can enrich datasets limited by underrepr...

HuBERT-ECG as a self-supervised foundation model for broad and scalable cardiac applications

Deep learning models have shown remarkable performance in electrocardiogram (ECG) analysis, but the ...

Phenotypic Selectivity of Artificial Intelligence-enhanced Electrocardiography in Cardiovascular Diagnosis and Risk Prediction

Artificial intelligence (AI)-enhanced electrocardiogram (ECG) models are designed to detect specific...

Fusion-Based Deep Learning Ensemble on MIT-BIH and PTB-XL ECG Databases for Enhanced Cardiac Diagnosis

Electrocardiogram (ECG) analysis plays a critical role in the early detection and diagnosis of cardi...

Estimating ascending aortic diameter from the electrocardiogram

In an analysis of 69,173 UK Biobank participants, we paired MRI-based measurements of the ascending ...

ECG classification with convolutional neural networks demonstrates resilience to sex-imbalances in data

Many ECG-AI models have been developed to predict a wide range of cardiovascular outcomes. The under...

Short-term Repeatability of Artificial Intelligence Estimated Electrocardiographic Age

Advancements in artificial intelligence have enabled estimation of cardiac age from raw ECG waveform...

Machine learning to classify left ventricular hypertrophy using ECG feature extraction by variational autoencoder

Traditional ECG criteria for left ventricular hypertrophy (LVH) have modest diagnostic yield. Develo...

Enhancing the Reliability of Resting ECGs via Deep Learning–Driven Motion Artifact Detection

This study presents a novel two-stage framework to enhance the reliability of resting electrocardiog...

A deep learning ECG model for localization of occlusion myocardial infarction

Rapid identification and localization of an acute coronary occlusion are vital to prevent myocardial...

Forecasting left ventricular systolic dysfunction in heart failure with artificial intelligence

Objective assessment of left ventricular function remains a key prognosticator that is used to guide...

BeatAI: BiomEtrics for Atrial Arrhythmia Tracking Using Artificial Intelligence

Postoperative atrial fibrillation (POAF) affects 20 to 50% of patients undergoing cardiac surgery an...

Privacy-Aware Federated nnU-Net for ECG Page Digitization

Deep neural networks can convert ECG page images into analyzable waveforms, yet centralized training...

Intelligent Decision Support System Facilitating Early Detection of Cardiovascular Disease

Cardiovascular disease (CVD) remains the primary cause of mortality worldwide, with higher fatality ...

A foundation transformer model with self-supervised learning for ECG-based assessment of cardiac and coronary function

The wide availability of labeled electrocardiogram (ECG) data has driven major advances in artificia...

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