Latest AI and machine learning research in arrhythmias for healthcare professionals.
The inherited arrhythmia (IA) syndromes are a group of rare and complex conditions that may predispose individuals to ventricular arrhythmias and sudden cardiac death. Our understanding of the genetic architecture underlying these syndromes has evolved, with recent reappraisals of variant pathogenicity and quantification of polygenic influences. The IA population includes an increasing proportion ...
OBJECTIVES: Most patients presenting with chest pain in the emergency medical services (EMS) setting are suspected of non-ST-elevation acute coronary syndrome (NSTE-ACS). Distinguishing true NSTE-ACS from non-cardiac chest pain based solely on the ECG is challenging. The aim of this study is to develop and validate a convolutional neural network (CNN)-based model for risk stratification of suspect...
This study aims to enhance the accuracy and efficiency of energy consumption prediction during exercise training and address the limitations of existi...
OBJECTIVE: We present the first multimodal deep learning framework combining ultrasound (US) and electrocardiography (ECG) data to predict cardiac qui...
: Early defibrillation improves outcomes in cardiac arrest, but the optimal defibrillation strategy and energy requirements remain debated. This study...
Fabry disease (FD) is a rare genetic disorder caused by mutations in the gene, affecting multiple organs. Over 60% of patients experience heart-relat...
BACKGROUND: Artificial intelligence (AI) is a modern tool that increases the diagnostic precision of the classical electrocardiogram (ECG). The object...
Brugada syndrome (BrS) is a cardiac channelopathy associated with an elevated risk of arrhythmias and sudden cardiac death compared with the general p...
Artificial intelligence (AI)-ECG-derived age (AI-ECG age) and Heart Delta Age (HDA)-the difference between AI-ECG and chronological age-are emerging t...
BACKGROUND: Various methods have been used to identify substrate of persistent atrial fibrillation (PeAF) including complex fractionated atrial electr...
The electrocardiogram (ECG) serves as a crucial tool for myocardial infarction (MI) localization, and deep learning methods have proven effective in a...
Early detection of atrial fibrillation (AFib) is crucial for altering its natural progression and complication profile. Traditional demographic and li...
Premature ventricular contractions (PVCs) are the most prevalent ventricular arrhythmia in adults. High PVC burden can lead to left ventricular (LV) s...
BACKGROUND: The electrocardiogram (ECG) screening in athletes is essential due to the unique cardiac adaptations induced by intensive training. Howeve...
Cardiac computed tomography (CCT) holds an important role in the field of electrophysiology offering critical insights that enhance the management of ...
Transcranial direct current stimulation (tDCS) as a non-invasive stimulation is still in the experimental stage for many psychiatric disorders even in...
OBJECTIVE: In this paper we develop and evaluate ECG-SMART-NET for occlusion myocardial infarction (OMI) identification. OMI is a severe form of heart...
Electrocardiogram (ECG) is a common non-invasive diagnostic tool for cardiovascular diseases. Adequate data is crucial in utilizing deep learning to a...
Arrhythmia classifiers relying on supervised deep learning models usually require a substantial amount of labeled clinical data. The distribution of t...
INTRODUCTION: CVDs is a leading cause of morbidity, mortality, and healthcare expenditure worldwide. Identifying individuals at risk or in the incipie...