IEEE journal of biomedical and health informatics
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IEEE J Biomed Health Inform · Jul 2019
Finite State Machine Framework for Instantaneous Heart Rate Validation Using Wearable Photoplethysmography During Intensive Exercise.
Accurate estimation of heart rate (HR) using reflectance-type photoplethysmographic (PPG) signals during intensive physical exercise is challenging because of very low signal-to-noise ratio and unpredictable motion artifacts (MA), which are frequently uncorrelated with reference signals, such as accelerometer signals. In this paper, we propose a finite state machine framework based novel algorithm for HR estimation and validation, which exploits the crest factor from the periodogram obtained after MA removal, and the estimated HR changes in consecutive windows as the estimation accuracy indicators. Our proposed algorithm automatically provides only accurate HR estimation results in real time by ignoring the estimation results when true HRs are not reflected in PPG signals or when the MAs uncorrelated with accelerometer signals are dominant. ⋯ Our algorithm exhibits an average absolute error of 0.99 beats per minute and an average relative error of 0.88%. The algorithm is simple; the computational time is [Formula: see text] for 8 s window. Also, the algorithm framework can be combined with existing methods to improve estimation accuracy.