Lee J, Chung H, Lee H. Multi-Mode Particle Filtering Methods for Heart Rate Estimation From Wearable Photoplethysmography.
IEEE Trans Biomed Eng 2019;
66:2789-2799. [PMID:
30703006 DOI:
10.1109/tbme.2019.2895685]
[Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
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.
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