AI-driven clustering and visualization of electrocardiogram signals to enhance screening for atrial fibrillation: The supermarket/hypermarket opportunistic screening for atrial fibrillation study.

ABSTRACT:

Background

Atrial fibrillation (AF) is the most common arrhythmia worldwide, associated with an increased risk of serious health issues. As its prevalence rises, health care systems face significant challenges, including escalating treatment costs and the inherent difficulties of detecting AF, particularly in paroxysmal cases where symptoms are intermittent.

Objective

This study investigates the application of unsupervised machine learning, specifically generative topographic mapping (GTM), to support AF screening and risk stratification.

Methods

The supermarket/hypermarket opportunistic screening for atrial fibrillation study deployed single-lead electrocardiogram (ECG) sensors (MyDiagnostick) embedded in supermarket trolley handles across 4 sites in Northwest Englan

SUBMITTER: Bellfield RAA 

PROVIDER: S-EPMC12570188 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature