A Motion-resistant Heart Rate Extracting Algorithm Based on Sparse Bayesian Decomposition
Wang Qun
Zhang Xun
Liu Zhiwen
Abstract：Objective To propose a method for heart rate monitoring during physical activities using photoplethysmography (PPG).Methods The spectrums of 2 channel PPG signals were estimated based on a Discrete Fourier Transform (DFT) sparse basis,which was modulated by the spectrum of one-channel simultaneously recorded acceleration signal.With a common sparsity constraint on the spectral coefficients,the method could easily identify and remove the spectral peaks of motion artifact (MA) in the PPG spectrum.Finally,the spectral peak corresponded to heart rate was searched from five kinds of feature points.Results Experiments were conducted on 12 groups of PPG signals.The average absolute estimation error was 1.99 beats/min and the standard deviation was 2.44 beats/min.Conclusion The method is of a good performance in PPG-based heart rate monitoring.The accuracy and the motion-resistant ability of this method are satisfactory.
Keywords：heart ratephotoplethysmography (PPG)sparse Bayesian decompositionacceleration signal
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 456-462 )