Multi-Mode Particle Filtering Methods for Heart Rate Estimation From Wearable Photoplethysmography

摘要

Objective: Obtaining accurate estimates of instantaneous heart rates (HRs) using reflectance-type photoplethysmography (PPG) sensors is challenging because the dominant frequency observed in the PPG signal can be corrupted by motion artifacts (MAs), especially during exercise. To address this problem, we propose multi-mode particle filtering (MPF) methods. Methods: We propose four MPF methods based on different approaches to particle weighting and HR determination. We compare the MPF performances with single-mode particle filtering and other state-of-the-art methods. Results: When applied to 47 PPG recordings obtained during intensive physical exercise from two different databases, the proposed MPF methods exhibit an average absolute error of less than two beats per minute, which is less than the errors of the SPF and other state-of-the-art methods. Furthermore, the MPF methods require only 6.4-6.5 ms in an 8 s window. Conclusion: The MPF methods significantly reduce the HR estimation error and can be implemented in real-time in practical applications. Significance: Our proposed MPF methods accurately estimate HRs even during intensive physical exercise, with robustness evidenced by their accuracy even when PPG signals are severely corrupted by MAs in several consecutive windows. The proposed methods can also be applied to other time-varying physiological feature-monitoring problems.