Cardiovascular

Arrhythmias

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

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Vib2ECG: A Paired Chest-Lead SCG-ECG Dataset and Benchmark for ECG Reconstruction

Twelve-lead electrocardiography (ECG) is essential for cardiovascular diagnosis, but its long-term a...

ECG-Reasoning-Benchmark: A Benchmark for Evaluating Clinical Reasoning Capabilities in ECG Interpretation

While Multimodal Large Language Models (MLLMs) show promising performance in automated electrocardio...

BALD-SAM: Disagreement-based Active Prompting in Interactive Segmentation

The Segment Anything Model (SAM) has revolutionized interactive segmentation through spatial prompti...

SignalMC-MED: A Multimodal Benchmark for Evaluating Biosignal Foundation Models on Single-Lead ECG and PPG

Recent biosignal foundation models (FMs) have demonstrated promising performance across diverse clin...

Echo2ECG: Enhancing ECG Representations with Cardiac Morphology from Multi-View Echos

Electrocardiography (ECG) is a low-cost, widely used modality for diagnosing electrical abnormalitie...

ECG Classification on PTB-XL: A Data-Centric Approach with Simplified CNN-VAE

Automated electrocardiogram (ECG) classification is essential for early detection of cardiovascular ...

Modeling and Control of a Pneumatic Soft Robotic Catheter Using Neural Koopman Operators

Catheter-based interventions are widely used for the diagnosis and treatment of cardiac diseases. Re...

Detecting Structural Heart Disease from Electrocardiograms via a Generalized Additive Model of Interpretable Foundation-Model Predictors

Structural heart disease (SHD) is a prevalent condition with many undiagnosed cases, and early detec...

An interpretable and explainable neural network to classify sports-related cardiac arrhythmias in professional football athletes

Sudden cardiac death risk is 2-3-fold higher in athletes than in non-athletes. We classify sports-re...

Chain of Flow: A Foundational Generative Framework for ECG-to-4D Cardiac Digital Twins

A clinically actionable Cardiac Digital Twin (CDT) should reconstruct individualised cardiac anatomy...

RhythmBERT: A Self-Supervised Language Model Based on Latent Representations of ECG Waveforms for Heart Disease Detection

Electrocardiogram (ECG) analysis is crucial for diagnosing heart disease, but most self-supervised l...

Learning geometry-dependent lead-field operators for forward ECG modeling

Modern forward electrocardiogram (ECG) computational models rely on an accurate representation of th...

AI-Detected Asymptomatic Atrial Fibrillation and Risk of Incident Ischemic Stroke and Cardiovascular Events: A UK Biobank Study

Background: Advances in wearable devices and machine-learning-based ECG analysis enable highly accur...

Position: Evaluation of ECG Representations Must Be Fixed

This position paper argues that current benchmarking practice in 12-lead ECG representation learning...

Wavelet-Domain Multi-Representation and Ensemble Learning for Automated ECG Analysis

Accurate diagnosis of cardiac abnormalities from electrocardiogram signals remains a central challen...

CAMEL: An ECG Language Model for Forecasting Cardiac Events

Electrocardiograms (ECG) are electrical recordings of the heart that are critical for diagnosing car...

Prediction of Left Atrial Volume Parameters from Resting ECGs and Tabular Data Using Deep Learning in the UK Biobank

We present a deep learning model that predicts left atrial (LA) volume from standard 12-lead ECG rec...

Dual-Phase Cross-Modal Contrastive Learning for CMR-Guided ECG Representations for Cardiovascular Disease Assessment

Cardiac magnetic resonance imaging (CMR) offers detailed evaluation of cardiac structure and functio...

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