用时间一致性提升可穿戴设备心率检测准确率
TAU: Modeling Temporal Consistency Through Temporal Attentive U-Net for PPG Peak Detection
- 引入时间注意力机制，融合时序一致性与信号幅值信息
- 在不同信噪比下均优于11个基线模型，心率估计提升超22.4%
- 适合低噪声环境下高精度心率变异性分析的场景
光电体积描记（PPG）传感器广泛用于消费级可穿戴设备中监测心率（HR）和心率变异性（HRV）。然而，日常活动引起的运动伪影常导致信号失真。现有方法多依赖信号幅值进行峰值检测，但运动伪影会引起幅度随机且显著变化，难以准确识别。为提升检测性能，本文利用心率具有时间一致性的特点——正常人连续心跳间隔变化有限。提出时间注意力U-Net（TAU），通过时间模块编码时序一致性并生成时序嵌入，结合注意力机制融合幅值信息与时序嵌入以预测峰值标签。实验表明，TAU在心率估计上超越11个基线模型超过22.4%；在各类信噪比（SNR）水平下表现最优；在低噪声条件下，心率变异性特征估计的皮尔逊相关系数高于0.9（p < 0.01）。
原文摘要 · Abstract (English)
Photoplethysmography (PPG) sensors have been widely used in consumer wearable devices to monitor heart rates (HR) and heart rate variability (HRV). Despite the prevalence, PPG signals can be contaminated by motion artifacts induced from daily activities. Existing approaches mainly use the amplitude information to perform PPG peak detection. However, these approaches cannot accurately identify peaks, since motion artifacts may bring random and significant amplitude variations. To improve the performance of PPG peak detection, the time information can be used. Specifically, heart rates exhibit temporal consistency that consecutive heartbeat intervals in a normal person can have limited variations. To leverage the temporal consistency, we propose the Temporal Attentive U-Net, i.e., TAU, to accurately detect peaks from PPG signals. In TAU, we design a time module that encodes temporal consistency in temporal embeddings. We integrate the amplitude information with temporal embeddings using the attention mechanism to estimate peak labels. Our experimental results show that TAU outperforms eleven baselines on heart rate estimation by more than 22.4%. Our TAU model achieves the best performance across various Signal-to-Noise Ratio (SNR) levels. Moreover, we achieve Pearson correlation coefficients higher than 0.9 (p < 0.01) on estimating HRV features from low-noise-level PPG signals.
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