Abstract
Photoplethysmography (PPG) has been widely used in noninvasive blood volume and blood flow detection since its first appearance. However, its noninvasiveness also makes the PPG signals vulnerable to noise interference and thus exhibits nonlinear and nonstationary characteristics, which have brought difficulties for the denoising of PPG signals. Ensemble empirical mode decomposition known as EEMD, which has made great progress in noise processing, is a noise-assisted nonlinear and nonstationary time series analysis method based on empirical mode decomposition (EMD). The EEMD method solves the “mode mixing” problem in EMD effectively, but it can do nothing about the “end effect,” another problem in the decomposition process. In response to this problem, an improved EEMD method based on support vector regression extension (SVR-EEMD) is proposed and verified by simulated data and real-world PPG data. Experiments show that the SVR-EEMD method can solve the “end effect” efficiently to get a better decomposition performance than the traditional EEMD method and bring more benefits to the noise processing of PPG signals.
Highlights
PPG [1] is a promising biometric technique based on Lambert–Beer’s law [2] and the difference in spectral absorption characteristics of human skin and blood to convert optical signals into blood volume and blood flow information
The noninvasiveness of PPG has both advantages and disadvantages: PPG signals are susceptible to disturbances from external environment and it causes inaccuracies to the measured results and those disturbances, including respiratory activities (RA), motion artifacts (MA), power line interference, and high-frequency noise generated by electronic components, tend to cause PPG signals to be doped with nonlinear and nonstationary components, which can result in spectral aliasing and distortion when processed with traditional methods
Sweeney et al [14] used EEMD with canonical correlation analysis to remove artifacts both from electroencephalography (EEG) and functional near infrared spectroscopy single channel data; Liao et al [15] used the EEMD method to achieve accurate analysis for PPG signals and implemented it on a specific platform; Chuang et al [16] analyzed the highfrequency band (0.4–0.9 Hz) of IMF5th decomposed by EEMD to measure pulse rate variability (PRV); Motin et al [17] proposed an algorithm based on EEMD with principal component analysis (EEMD-PCA) as a novel approach to Computational and Mathematical Methods in Medicine estimate heart rate (HR) and respiratory rate (RR) simultaneously from PPG signals; Sadrawi et al [18] used PPG data corrupted by vertical MA noise to evaluate the performance of EEMD filtering
Summary
PPG [1] is a promising biometric technique based on Lambert–Beer’s law [2] and the difference in spectral absorption characteristics of human skin and blood to convert optical signals into blood volume and blood flow information. It can be used for noninvasive detection of microvascular blood flow changes, providing quantities of possibilities in detecting blood volume and blood flow parameters [3,4,5]. Sweeney et al [14] used EEMD with canonical correlation analysis to remove artifacts both from electroencephalography (EEG) and functional near infrared spectroscopy (fNIRS) single channel data; Liao et al [15] used the EEMD method to achieve accurate analysis for PPG signals and implemented it on a specific platform; Chuang et al [16] analyzed the highfrequency band (0.4–0.9 Hz) of IMF5th decomposed by EEMD to measure pulse rate variability (PRV); Motin et al [17] proposed an algorithm based on EEMD with principal component analysis (EEMD-PCA) as a novel approach to Computational and Mathematical Methods in Medicine estimate heart rate (HR) and respiratory rate (RR) simultaneously from PPG signals; Sadrawi et al [18] used PPG data corrupted by vertical MA noise to evaluate the performance of EEMD filtering
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