Arrhythmias

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 channelopathies are at increased risk due to their underlying electrophysiological vulnerability. OBJECTIVES: We aimed to develop a machine learning (ML) platform for disease-specific cardiotoxicity using patient-specific induced pluripotent stem cell-deri...

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 proposes ModelS4Apnea, a deep learning framework for sleep apnea detection from electrocardiogram (ECG) spectrograms, integrating structured state space models (S4) for temporal modeling. The framework consists of a convolutional neural network module fo...

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 Personalized Models and Differential Privacy (FLPMDP)...

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-defibrillators (ICDs) is critical for guiding thei...

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 for accurate and early diagnosis. Electrocardiograms...

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 clinical presentation. It is associated with signif...

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 clinical applications. Although deep learning model...

Exploring advanced deep learning approaches in cardiac image analysis: A comprehensive review.

BACKGROUND: Cardiac image analysis plays an important role in detecting and categorizing cardiovascular diseases (CVDs), such as coronary artery disea...

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. This study evaluated whether artificial intelligenc...

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 respiratory patterns during sleep. This disruption can hav...

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 antiarrhythmic drugs (AADs) play a crucial role in managing AF...

Semantic ECG hash similarity graph.

Graph-based methods have made significant progress in addressing the dependent correlations among ECG time series variables. However, most existing gr...

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 electrocardiography (ECG; AI-ECG) algorithm to predict atrial fibri...

Multimodal AI to forecast arrhythmic death in hypertrophic cardiomyopathy.

Sudden cardiac death from ventricular arrhythmias is a leading cause of mortality worldwide. Arrhythmic death prognostication is challenging in patien...

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

Generative Counterfactual Explainable Artificial Intelligence (XAI) offers a novel approach to understanding how AI models interpret electrocardiogram...

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 nanomedicines that synergistically combine anti-tumor and an...

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 intervention in cardiac disorders. Existing models, ...

Overcoming data scarcity in life-threatening arrhythmia detection through transfer learning.

BACKGROUND: Life-threatening arrhythmias (LTAs) are a leading cause of death worldwide. Enhancing LTA detection in wearable monitoring systems is of g...

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 put forward a scientific evaluation method, this st...