PaperarXiv:2511.00949
Motion-Robust Multimodal Fusion of PPG and Accelerometer Signals for Three-Class Heart Rhythm Classification
arXiv preprint (cs.LG) · Accepted for publication in the Companion of the 2025 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2025 International Symposium on Wearable Computers (UbiComp/ISWC 2025 Companion). 5 pages, 3 figures. Author's accepted manuscript (AAM)
TL;DR
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.
- Form factor: wrist
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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APA
Zhao, Y., Kaisti, M., Lahdenoja, O., & Koivisto, T. (2025). Motion-Robust Multimodal Fusion of PPG and Accelerometer Signals for Three-Class Heart Rhythm Classification. arXiv preprint · Accepted for publication in the Companion of the 2025 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2025 International Symposium on Wearable Computers . 5 pages, 3 figures. Author's accepted manuscript. https://doi.org/10.48550/arXiv.2511.00949
BibTeX
@misc{zhao2025motionrobust,
title = {{Motion-Robust Multimodal Fusion of PPG and Accelerometer Signals for Three-Class Heart Rhythm Classification}},
author = {Zhao, Yangyang and Kaisti, Matti and Lahdenoja, Olli and Koivisto, Tero},
year = {2025},
howpublished = {arXiv preprint · Accepted for publication in the Companion of the 2025 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2025 International Symposium on Wearable Computers . 5 pages, 3 figures. Author's accepted manuscript},
doi = {10.48550/arXiv.2511.00949},
eprint = {2511.00949},
archivePrefix = {arXiv}
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