用智能手表数据提升心律失常分类精度，尤其大幅改善早搏检测灵敏度。
Multiclass Arrhythmia Classification using Smartwatch Photoplethysmography Signals Collected in Real-life Settings
- 融合心率、加速度计和一维光体积描记信号，用轻量1D-Bi-GRU模型实现多类分类。
- 早搏检测灵敏度达83%，房颤识别准确率97.31%，均优于现有最佳模型。
- 模型更轻更快，适合部署在资源受限的可穿戴设备上。
大多数多类心律失常分类的深度学习模型基于指尖光体积描记（PPG）数据测试，其信噪比高于智能手表采集的PPG数据。目前最优的早搏（PAC/PVC）检测灵敏度仅为75%。为提升早搏检测灵敏度并保持高房颤（AF）检测性能，我们采用多模态数据——包括一维PPG、加速度计和心率数据——输入计算高效的1D双向门控循环单元（1D-Bi-GRU）模型，以检测三类心律失常。实验使用来自美国国立卫生研究院资助的Pulsewatch临床试验中72名受试者的真实生活场景智能手表PPG数据。该多模态模型在测试中实现了前所未有的83%早搏检测灵敏度，同时保持97.31%的房颤检测准确率。相比现有最佳模型，早搏检测性能提升20.81%，房颤检测提升2.55%，且模型重量减轻14倍，推理速度提升2.7倍。
原文摘要 · Abstract (English)
Most deep learning models of multiclass arrhythmia classification are tested on fingertip photoplethysmographic (PPG) data, which has higher signal-to-noise ratios compared to smartwatch-derived PPG, and the best reported sensitivity value for premature atrial/ventricular contraction (PAC/PVC) detection is only 75%. To improve upon PAC/PVC detection sensitivity while maintaining high AF detection, we use multi-modal data which incorporates 1D PPG, accelerometers, and heart rate data as the inputs to a computationally efficient 1D bi-directional Gated Recurrent Unit (1D-Bi-GRU) model to detect three arrhythmia classes. We used motion-artifact prone smartwatch PPG data from the NIH-funded Pulsewatch clinical trial. Our multimodal model tested on 72 subjects achieved an unprecedented 83% sensitivity for PAC/PVC detection while maintaining a high accuracy of 97.31% for AF detection. These results outperformed the best state-of-the-art model by 20.81% for PAC/PVC and 2.55% for AF detection even while our model was computationally more efficient (14 times lighter and 2.7 faster).
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