Arrhythmias

Predicting Heart Failure From 12-Lead ECGs Using AI: A HeartShare/AMP-HF Pooled Cohort Analysis.

BACKGROUND: Artificial intelligence applied to electrocardiograms (ECG-AI) offers a scalable approach to identify individuals at risk for heart failure (HF) and guide preventive interventions. OBJECTIVES: The purpose of this study was to assess whether ECG-AI designed to detect systolic and diastolic dysfunction enhances the prediction of incident HF over clinical risk estimation using the PREVENT...

Exploring latent diffusion models for ECG generation on the minute scale.

BACKGROUND AND OBJECTIVE: Artificial Intelligence (AI) models for electrocardiogram (ECG) interpretation rely on large, diverse datasets, but existing clinical datasets are often skewed, overrepresenting normal rhythms and lacking rare pathologies. This limits model performance and generalizability. While generative AI has shown promise in other domains, its application to biosignals has so far be...

Multimodal deep learning for acute myocardial infarction detection from 12-lead electrocardiogram: a multi-centre study with cross-hospital validation.

AIMS: Acute myocardial infarction (AMI) remains a leading global cause of mortality, where timely diagnosis is critical to enable early intervention. ...

Multi-modal deep-learning troponin prediction from electrocardiograms and demographic data.

AIMS: Electrocardiograms (ECGs) and troponin (Tn) testing are essential tools for the diagnosis and management of cardiac conditions. Prompt diagnosis...

Machine learning-enabled assessment of risk of drug-induced QT prolongation at the time of prescribing.

BACKGROUND: Many medications are associated with long QTc. Current long QTc predictors have limited generalizability and/or modest performance. OBJECT...

Real-Time Phase-Contrast Cardiovascular MRI Using a Deep Learning Reconstruction Network With Combined Dictionary Learning and CNN.

PURPOSE: This study aims to develop real-time phase-contrast (PC) cardiovascular MRI with low latency. METHODS: In this study, a framework using golde...

Sleep disturbance recorded via wearable sensors predicts depression severity 9 years later.

BACKGROUND: Major depressive disorder (MDD) is prevalent and poses major public health implications. Autonomic nervous system (ANS) dysregulation and ...

Feature extraction and intelligent diagnosis of ECG signals based on KANs and xLSTM.

Cardiovascular disease (CVD) is the top cause of mortality globally, making it crucial to diagnose arrhythmias promptly and accurately for the early p...

Image based artificial intelligence-enhanced electrocardiogram prediction of incident atrial fibrillation.

BACKGROUND: Early prediction of atrial fibrillation (AF) is crucial for reducing adverse outcomes. While artificial intelligence-enhanced electrocardi...

Newton-Puiseux analysis for interpretability and calibration of complex-valued neural networks.

Complex-valued neural networks (CVNNs) are particularly suitable for handling phase-sensitive signals, including electrocardiography (ECG), radar/sona...

GDT-Net: Multi-level feature extraction network for precise diagnosis of atrial and ventricular fibrillation.

Atrial fibrillation (AFIB) and ventricular fibrillation (VFIB) are two critical cardiovascular diseases, where accurate diagnosis is essential for tim...

Multimodal, device-based therapeutic targeting of the cardiovascular autonomic nervous system.

The miniaturization of implantable sensors and actuators, combined with advances in interactive modelling and high-resolution imaging, is propelling t...

GAN-based novel feature selection approach with hybrid deep learning for heartbeat classification from ECG signal.

Heart arrhythmias are one of the most important categories of cardiovascular illness. A heartbeat that is abnormal like too early, too slow, too fast,...

Deep Learning Predicts Cardiac Output from Seismocardiographic Signals in Heart Failure.

Determination of cardiac output (CO) is essential to the clinical management of cardiovascular compromise. However, the invasiveness, procedural risks...

Driver Emotion Recognition Using Multimodal Signals by Combining Conformer and Autoformer.

This study aims to develop a multimodal driver emotion recognition system that accurately identifies a driver's emotional state during the driving pro...

hERG-MFFGNN: An Explainable Deep Learning Model for Predicting Cardiotoxicity Using Multi-feature Fusion and Graph Neural Networks.

Drug-related cardiotoxicity, most notably arrhythmia, represents a major challenge in drug development. Inhibition of hERG potassium channel by certai...

A New Method of Modeling the Multi-stage Decision-Making Process of CRT Using Machine Learning with Uncertainty Quantification.

Current machine learning-based (ML) models usually attempt to utilize all available patient data to predict patient outcomes while ignoring the associ...

BM3D filtering with Ensemble Hilbert-Huang Transform and spiking neural networks for cardiomegaly detection in chest radiographs.

Cardiomyopathy is a life-threatening condition associated with heart failure, arrhythmias, thromboembolism, and sudden cardiac death, posing a signifi...

A machine learning platform for genotype-specific cardiotoxicity risk prediction using patient-derived iPSC-CMs.

INTRODUCTION: Drug-induced Torsades de Pointes (TdP) has led to withdrawal of several drugs from the market. Individuals with inherited cardiac channe...

ModelS4Apnea: leveraging structured state space models for efficient sleep apnea detection from ECG signals.

. Sleep apnea is a common sleep disorder associated with severe health risks, necessitating accurate and efficient detection methods.. This study prop...