Arrhythmias

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...

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 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 ...

Differentiating pilot distress and eustress via multimodal physiology: towards enhanced human-system integration in intelligent cockpits.

Ensuring safety in next-generation intelligent cockpits demands accurate assessment of pilot states,...

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...

Fusion of Personalized Federated Learning (PFL) with Differential Privacy (DP) Learning for Diagnosis of Arrhythmia Disease.

This paper presents a novel privacy-preserving architecture, a fusion of Federated Learning with Per...

Artificial intelligence-enhanced ECG detection of acute coronary occlusion in chest pain patients with ST-elevation in lead aVR - A direct comparison to conventional ECG criteria.

Machine Learning-Based Prognostic Models for Mortality in Patients Receiving Implantable Cardioverter Defibrillators.

BACKGROUND: Accurately predicting the clinical trajectory of patients with implantable cardioverter-...

Efficient pretraining of ECG scalogram images using masked autoencoders for cardiovascular disease diagnosis.

Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide, emphasizing the need fo...

Artificial intelligence in cardiac sarcoidosis: ECG, Echo, CPET and MRI.

PURPOSE OF REVIEW: Cardiac sarcoidosis is a form of inflammatory cardiomyopathy that varies in its c...

Enhancing automatic multilabel diagnosis of electrocardiogram signals: A masked transformer approach.

BACKGROUND AND OBJECTIVE: Electrocardiogram (ECG) is one of the most important diagnostic tools in c...

Artificial Intelligence-Enabled ECGs for Atrial Fibrillation Identification and Enhanced Oral Anticoagulant Adoption: A Pragmatic Randomized Clinical Trial.

BACKGROUND: Atrial fibrillation (AF) is often underdiagnosed and undertreated by noncardiologists. T...

Efficient sleep apnea detection using single-lead ECG: A CNN-Transformer-LSTM approach.

BACKGROUND: Sleep apnea (SA), a prevalent sleep-related breathing disorder, disrupts normal respirat...

Digital twin for sex-specific identification of class III antiarrhythmic drugs based on in vitro measurements, computer models, and machine learning tools.

Atrial fibrillation (AF) significantly affects morbidity and mortality rates. Class III antiarrhythm...

Semantic ECG hash similarity graph.

Graph-based methods have made significant progress in addressing the dependent correlations among EC...

Artificial Intelligence-Enhanced Electrocardiography for Prediction of Occult Atrial Fibrillation in Patients With Stroke Who Undergo Prolonged Cardiac Monitoring.

OBJECTIVE: To evaluate the performance of an artificial intelligence (AI)-enhanced electrocardiograp...

Multimodal AI to forecast arrhythmic death in hypertrophic cardiomyopathy.

Sudden cardiac death from ventricular arrhythmias is a leading cause of mortality worldwide. Arrhyth...

A novel XAI framework for explainable AI-ECG using generative counterfactual XAI (GCX).

Generative Counterfactual Explainable Artificial Intelligence (XAI) offers a novel approach to under...

Driving Multifunctional Nanomedicine Design for Non-Inflammatory Tumor Therapy with Integrated Machine Learning and Density Functional Theory.

Due to the promotive role of inflammation in tumor progression, designing multifunctional nanomedici...

DeepECG-Net: a hybrid transformer-based deep learning model for real-time ECG anomaly detection.

Real-time Electrocardiogram (ECG) anomaly detection is critical for accurate diagnosis and timely in...

Machine learning evaluation model of pilot workload in a low-visibility environment.

To analyze the variation trend of pilots' workload in a low-visibility flight environment and then p...