TAU: Modeling Temporal Consistency Through Temporal Attentive U-Net for PPG Peak Detection
This work addresses a critical issue in wearable health monitoring by improving peak detection accuracy, though it appears incremental as it builds on existing U-Net and attention mechanisms for a specific domain.
The paper tackles the problem of inaccurate PPG peak detection due to motion artifacts by proposing TAU, a model that leverages temporal consistency through a Temporal Attentive U-Net, resulting in over 22.4% improvement in heart rate estimation compared to baselines and Pearson correlation coefficients above 0.9 for HRV features.
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.