PPG heart rate extraction algorithm based on the motion artifact intensity Classification and removal framework

Abstract

When heart rate is extracted by wearable PPG, motion artifact noise causes serious interference. Since the accuracy of PPG heart rate extraction tends to be positively correlated with the complexity of heart rate extraction algorithm, it is challenging to extract heart rate via wearable PPG throughout the day by taking into account both algorithmic complexity and the accuracy of heart rate extraction. In this paper, a PPG heart rate extraction algorithm is proposed under the motion artifact intensity classification and removal (MAICR) framework. Firstly, it evaluates the motion artifact intensity using acceleration time-domain information and PPG frequency-domain information. Then, different heart rate estimation methods are selected according to motion artifact intensity, in ascending order. This corresponds to the use of the frequency-domain direct extraction method, the MNLMS adaptive filtering method, and the joint sparse spectral reconstruction method. Additionally, the overall framework of the algorithm (MAICR) is developed by considering the performance of various methods. Finally, experiments are conducted on the public dataset to demonstrate that the proposed algorithm is applicable to reduce the algorithmic complexity effectively while maintaining the accuracy in heart rate extraction at a certain degree.