Transformer-Based Detection of Cardiac Arrhythmias from Ambulatory Photoplethysmography Signals
Keywords:
transformer models; photoplethysmography; arrhythmia detection; ambulatory monitoring; health informatics; algorithmic fairnessAbstract
Cardiac arrhythmias impose significant morbidity and mortality, with atrial fibrillation contributing to stroke, heart failure, and cognitive impairment. Ambulatory electrocardiography remains the reference standard for rhythm diagnosis, but prolonged use is constrained by electrode adhesion, patient burden, and cost. Photoplethysmography embedded in consumer wearables provides a lower-burden alternative for continuous cardiovascular monitoring. However, motion artifact, sensor heterogeneity, and algorithmic opacity limit clinical translation. This paper presents a system-level analysis of transformer-based detection of cardiac arrhythmias from ambulatory photoplethysmography signals. It examines sensing infrastructure, data governance, signal quality, architectural trade-offs, robustness, fairness, deployment, and policy implications. The discussion positions transformer models not only as sequence classifiers but as components within a broader sociotechnical monitoring pipeline requiring careful integration with preprocessing, explainability, regulatory oversight, and postmarket surveillance. The analysis emphasizes that performance improvements in offline datasets are insufficient for sustainable clinical utility. Attention to energy consumption, data drift, demographic equity, and governance is equally important. The paper synthesizes developments in time-series attention, motion robustness, and responsible machine learning to outline a forward-looking framework for next-generation ambulatory cardiac monitoring systems.
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