融合心率与运动信号，提升可穿戴设备对三类心律失常的识别准确率。
Motion-Robust Multimodal Fusion of PPG and Accelerometer Signals for Three-Class Heart Rhythm Classification
- 利用PPG与加速度计信号，通过注意力机制联合建模提升鲁棒性。
- 在三种心律分类中，宏平均AUC比纯PPG模型提升4.3%。
- 适合关注真实场景下可穿戴设备心律监测的临床研究者。
房颤是老年人卒中和死亡的主要原因之一。腕部光电容积脉搏波（PPG）可实现无创、持续的心律监测，但极易受运动伪影和生理噪声影响。现有方法多依赖单通道PPG，仅限于二分类房颤检测，难以覆盖临床中的多种心律失常。本文提出RhythmiNet，一种结合时间与通道注意力的残差神经网络，联合使用PPG与加速度计（ACC）信号进行三类心律分类：房颤（AF）、窦性心律（SR）及其他。为评估不同运动强度下的鲁棒性，测试数据按加速度计测得的运动强度分位数分层，不剔除任何片段。RhythmiNet相较纯PPG基线模型，宏平均AUC提升4.3%；同时相比基于人工提取心率变异性（HRV）特征的逻辑回归模型，性能高出12%，验证了多模态融合与注意力学习在真实世界噪声数据中的优势。
原文摘要 · Abstract (English)
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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