Arrhythmias

SLPM: a lightweight deep learning model for end-to-end paper ECG digitization.

OBJECTIVE: The digitization of paper electrocardiograms (ECGs) faces several challenges, including amplified errors during segmentation and signal extraction, severe noise interference, and poor generalization under complex conditions. To address these issues, we propose an end-to-end Signal Location Prediction Model (SLPM). APPROACH: SLPM employs a classification-regression joint learning framewo...

Use of Electrocardiograms to Identify Coronary Artery Disease: Cross-Validation of an Artificial Intelligence Model.

BACKGROUND: The current gold standard for the diagnosis of coronary artery disease (CAD) is invasive angiography; however, it is an invasive procedure. Therefore, we developed an artificial intelligence model designed to predict significant CAD from a resting digital 12-lead electrocardiogram (ECG). OBJECTIVES: This retrospective study assessed the model's ability to predict clinically significant...

Roof-dependent atrial tachycardia after pulsed field ablation-mechanistic insights and predictive modeling for optimized ablation strategies: an ultra-high-density mapping study.

BACKGROUND AND AIMS: Pulsed field ablation (PFA) has emerged to an innovative approach to achieve pulmonary vein isolation (PVI) in atrial fibrillatio...

A hybrid CNN and reinforcement learning framework for speaker identification using Mel-Spectrogram and continuous wavelet transform features.

Speaker identification remains critical in biometric authentication systems, requiring robust feature extraction strategies that capture speaker-speci...

Unbiased Characterization of Atrial Fibrillation Phenotypic Architecture Provides Insight Into Genetic Liability and Clinically Relevant Outcomes.

BACKGROUND: Atrial fibrillation (AF) is a common and clinically heterogeneous arrhythmia. Machine learning algorithms can define data-driven disease s...

6G conditioned spatiotemporal graph neural networks for real time traffic flow prediction.

Accurate, low-latency traffic forecasting is a cornerstone capability for next-generation Intelligent Transportation Systems (ITS). This paper investi...

Fast cardiac magnetic resonance (CMR) protocol for biventricular functional assessment and tissue characterisation.

BACKGROUND: Clinical use of cardiovascular magnetic resonance (CMR), reference tool for cardiac function and myocardial tissue assessment, is frequent...

Biatrial cardioneuroablation guided by robotic magnetic navigation and artificial intelligence-based mapping in vagal bradyarrhythmias: A controlled observational study.

PURPOSE: Reflex bradyarrhythmias and syncope related to excessive vagal tone may be refractory to conservative therapy and significantly impair qualit...

[Wolff-Parkinson-White syndrome : Comparison of different algorithms].

BACKGROUND: Wolff-Parkinson-White (WPW) syndrome is characterised by accessory pathways that bypass the normal atrioventricular conduction system. Pre...

Foundation models for electrocardiogram interpretation: clinical implications.

BACKGROUND AND AIMS: The 12-lead electrocardiogram (ECG) remains a cornerstone of cardiac diagnostics, yet existing artificial intelligence (AI) solut...

Contemporary implantable cardiac device trials for sudden cardiac death and future guidelines: how can we shape the future?

INTRODUCTION: Sudden cardiac death (SCD) remains a major cause of mortality despite substantial progress in heart failure management and arrhythmia pr...

A 1.69µJ Highly Robust Cardiac Arrhythmia Monitoring Processor with Triple-Adaptive QRS Detector and Medically Driven Feature-Fusion Hybrid Neural Networks.

The detection of arrhythmias is crucial in monitoring cardiac health. However, electrocardiogram (ECG) signals obtained from wearable devices are ofte...

Domain Knowledge is Power: Leveraging Physiological Priors for Self-Supervised Representation Learning in Electrocardiography.

Electrocardiograms (ECGs) play a crucial role in diagnosing heart conditions; however, the effectiveness of artificial intelligence (AI)-based ECG ana...

Data-driven pediatric ECG reference intervals with VSD-based validation.

To establish population-specific, age- and sex-stratified electrocardiographic (ECG) reference ranges for Chinese children and adolescents using a dat...

A hybrid hierarchical transformer model for ECG classification and age prediction.

Electrocardiograms (ECGs) play a crucial role in cardiovascular healthcare, requiring effective analytical models. ECG analysis is inherently hierarch...

Artificial Intelligence-Enhanced Electrocardiogram Models for Detection of Left Ventricular Dysfunction: A Comparison Study.

BACKGROUND: Several artificial intelligence-enhanced electrocardiogram (AI-ECG) models have shown promise in detecting left ventricular systolic dysfu...

[Enhancement of radiomics-based machine learning models for predicting efficacy of high-intensity focused ultrasound ablation of uterine fibroids using undersampling methods].

OBJECTIVES: To improve the accuracy of machine learning models for preoperative prediction of high-intensity focused ultrasound (HIFU) ablation effica...

[Evaluation of an interpretable 12-lead ECG automatic diagnosis model based on deep feature fusion].

OBJECTIVES: To enhance the accuracy and reliability of 12-lead electrocardiogram (ECG) automatic diagnosis. METHODS: Herein we propose a 12-lead ECG a...

Mapping Algorithmic Bias in AI-Powered Electrocardiogram Interpretation Across the AI Life Cycle: Protocol for a Scoping Review.

BACKGROUND: Artificial intelligence (AI)-powered analysis of electrocardiograms (ECGs) is reshaping cardiac diagnostics, offering faster and often mor...

uPVC-Net: A Universal Premature Ventricular Contraction Detection Deep Learning Algorithm.

INTRODUCTION: Premature Ventricular Contractions (PVCs) are common cardiac arrhythmias originating from the ventricles. Accurate detection remains cha...