Photoplethysmography-Based Heart Rate Monitoring Using Asymmetric Least Squares Spectrum Subtraction and Bayesian Decision Theory

Motion artifacts (MAs) are strong interference sources in wearable photoplethysmography (PPG) signals, significantly affecting estimation of heart rate (HR) and other physiological parameters. In this paper, a novel method called SPECTRAP is proposed for accurate motion-tolerant estimation of HR using a PPG signal and a simultaneous acceleration signal. The method first calculates the spectra of the PPG signal and the acceleration signal, and then removes the MA spectral components from the PPG spectrum using a new spectrum subtraction algorithm. The new spectrum subtraction algorithm is based on asymmetric least square and overcomes drawbacks of the conventional spectrum subtraction algorithms. To find the spectral peak corresponding to HR on the resulting spectrum, SPECTRAP formulates the problem into a pattern classification problem and uses the Bayesian decision theory to solve it. Experimental results on the PPG database used in 2015 IEEE Signal Processing Cup showed that the proposed algorithm has excellent performance. The average absolute error on the twelve training sets was 1.50 beat per minute (BPM) (standard deviation: 1.95 BPM). The average absolute error on the ten testing sets was 2.13 BPM (standard deviation: 2.77 BPM).