Motion-Robust Multimodal Fusion of PPG and Accelerometer Signals for Three-Class Heart Rhythm Classification

• URL: http://arxiv.org/abs/2511.00949v1
• Date: Sun, 02 Nov 2025 14:21:55 GMT
• Title: Motion-Robust Multimodal Fusion of PPG and Accelerometer Signals for Three-Class Heart Rhythm Classification
• Authors: Yangyang Zhao, Matti Kaisti, Olli Lahdenoja, Tero Koivisto,
• Abstract summary: Atrial fibrillation (AF) is a leading cause of stroke and mortality, particularly in elderly patients.<n>We introduce RhythmiNet, a residual neural network enhanced with temporal and channel attention modules.<n>The model performs three-class rhythm classification: AF, sinus rhythm (SR) and Other.
• Score: 5.573626931805866
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Atrial fibrillation (AF) is a leading cause of stroke and mortality, particularly in elderly patients. Wrist-worn photoplethysmography (PPG) enables non-invasive, continuous rhythm monitoring, yet suffers from significant vulnerability to motion artifacts and physiological noise. Many existing approaches rely solely on single-channel PPG and are limited to binary AF detection, often failing to capture the broader range of arrhythmias encountered in clinical settings. We introduce RhythmiNet, a residual neural network enhanced with temporal and channel attention modules that jointly leverage PPG and accelerometer (ACC) signals. The model performs three-class rhythm classification: AF, sinus rhythm (SR), and Other. To assess robustness across varying movement conditions, test data are stratified by accelerometer-based motion intensity percentiles without excluding any segments. RhythmiNet achieved a 4.3% improvement in macro-AUC over the PPG-only baseline. In addition, performance surpassed a logistic regression model based on handcrafted HRV features by 12%, highlighting the benefit of multimodal fusion and attention-based learning in noisy, real-world clinical data.

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