Latest AI and machine learning research in arrhythmias for healthcare professionals.
OBJECTIVE: To develop and validate a multi-lead electrocardiogram (ECG)-based machine learning syste...
Glomerular crescent lesions are critical indicators of severe kidney injury and are closely associat...
Electrocardiogram (ECG) reconstruction from reduced-lead configurations is essential for improving p...
BACKGROUND: Artificial intelligence (AI)-based electrocardiogram (ECG) analysis has emerged as a pro...
Convolutional neural networks (CNNs) can estimate electrocardiogram (ECG)-based heart age. We compar...
Intraoperative cardiac arrhythmias present distinct characteristics compared to non-surgical environ...
BACKGROUND: Stressor-associated atrial fibrillation (AF) refers to new-onset AF that occurs with a r...
BACKGROUND: Approximately 3.8 billion people lack access to essential health services, and diagnosti...
AIMS: Biological age is increasingly recognized as a superior predictor of morbidity, mortality, com...
Blood glucose monitoring is fundamental to diabetes management, yet traditional invasive methods are...
BACKGROUND: Predictive medicine relies on algorithms to determine clinical treatments tailored to ea...
OBJECTIVE: This work aims to enable adaptive Consumer Sleep Technologies (CSTs) for sleep interventi...
Chronic stress is an important threat in Public Health, as it negatively impacts both the Body and M...
Artificial intelligence (AI) is reshaping cardiac electrophysiology by extracting information from e...
BACKGROUND: Asymptomatic left ventricular systolic dysfunction (LVSD) is a well-established precurso...
In this paper, we present a memory-efficient ECG based heartbeat classification for wearable devices...
BACKGROUND: Predicting recurrence after pulsed field ablation (PFA) for paroxysmal atrial fibrillati...
\textbf{Objective:} Physiological measurements obtained from wearable devices reflect complex au...
Atrial fibrillation (AF) has traditionally been classified by episode duration, whereas rhythm contr...
OBJECTIVE: To explore the predictive value of machine learning-based multimodal MRI radiomics combin...
AIMS: Attribution-based explainability methods are widely used in electrocardiogram (ECG) analysis t...