Heart rhythm O2 · 2025
AI-driven clustering and visualization of electrocardiogram signals to enhance screening for atrial fibrillation: The supermarket/hypermarket opportunistic screening for atrial fibrillation study.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Bellfield Ryan A A, Hormiga Pablo Rendon, Olier Ivan, Lotto Robyn, Jones Ian, Lip Gregory Y H, Ortega-Martorell Sandra
Аннотация
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 England. This community-based approach successfully engaged the public in opportunistic AF screening. However, diagnosis was limited by reliance on transient ECG recordings. To improve analysis, we selected a subset of 97 ECG traces (78 for training and 19 for testing) reviewed by a consultant cardiologist, comprising AF (n = 23), possible AF (n = 9), and normal rhythm (n = 65). From these, 477 20-second ECG snippets were extracted to train the GTM model.
RESULTS: The GTM generated interpretable membership maps, clustering ECG snippets into visually distinct regions with similar features. These maps enable clinicians to explore heart rhythm dynamics over time and track patient trajectories across risk states.
CONCLUSION: This study demonstrates the potential of our proposed methodology to uncover latent patterns in ECG data, providing deeper insights into individual heart rhythm patterns and supporting more nuanced AF risk assessment and the overall effectiveness of AF detection and management. By embedding interpretable artificial intelligence in screening tools, we aimed to improve early detection and reduce the clinical burden of AF.
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 England. This community-based approach successfully engaged the public in opportunistic AF screening. However, diagnosis was limited by reliance on transient ECG recordings. To improve analysis, we selected a subset of 97 ECG traces (78 for training and 19 for testing) reviewed by a consultant cardiologist, comprising AF (n = 23), possible AF (n = 9), and normal rhythm (n = 65). From these, 477 20-second ECG snippets were extracted to train the GTM model.
RESULTS: The GTM generated interpretable membership maps, clustering ECG snippets into visually distinct regions with similar features. These maps enable clinicians to explore heart rhythm dynamics over time and track patient trajectories across risk states.
CONCLUSION: This study demonstrates the potential of our proposed methodology to uncover latent patterns in ECG data, providing deeper insights into individual heart rhythm patterns and supporting more nuanced AF risk assessment and the overall effectiveness of AF detection and management. By embedding interpretable artificial intelligence in screening tools, we aimed to improve early detection and reduce the clinical burden of AF.