A novel motion artifact removal method via joint basis pursuit linear program to accurately monitor heart rate

Photoplethysmography (PPG)-based heart rate (HR) estimation during physical exercise is challenging as PPG signals are often contaminated by motion artifacts (MA). This study develops a novel HR estimation method to effectively attenuate the impact of MA on PPG signals and accurately identify HR variations during physical exercise. First, a new signal reconstruction procedure is implemented based on a joint basis pursuit linear program (BPLP) to decompose PPG signal into different time series. Furthermore, an adaptive MA removal technique is developed, where the correlation between the acceleration signals and PPG time series are calculated and used as a reference to eliminate MA. Then, a new sparse spectra reconstruction method is designed to rebuild the spectrum of the current window based on the previous time frame. Furthermore, a simple HR estimation method with only one tuning parameter is designed to select the HR associated peak from the reconstructed spectra. Finally, a postprocessing technique is applied to further boost the accuracy of detection. The performance of the proposed algorithm is compared with three popular methods in recent studies using both training and testing sets from 2015 IEEE Signal Processing Cup. The proposed method provides the average absolute error of 1.79 beats per minutes (BPM) on all 22 recordings. With respect to testing datasets with stronger MA, the average absolute error is computed as 2.61BPM. The proposed HR tracking algorithm shows good robustness as it only involves a small set of parameters and can provide accurate estimations when PPG signals are contaminated by strong MA.