Denoising and implementation of photoplethysmography signal based on EEMD and wavelet threshold

被引：3

作者：

Chen Z.-C. ^{[1
]}

Wu X.-L. ^{[1
]}

Zhao F.-J. ^{[2
]}

机构：

[1] School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin

[2] School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin

来源：

Guangxue Jingmi Gongcheng/Optics and Precision Engineering | 2019年 / 27卷 / 06期

关键词：

Denoising; Ensemble empirical mode decomposition; Photoplethysmography; Wavelet threshold;

D O I：

10.3788/OPE.20192706.1327

中图分类号：

学科分类号：

摘要：

Signals of interest can be affected by various types of noise during the acquisition of photoplethysmography data. To address this problem, a denoising method based on the combination of Ensemble Empirical Mode Decomposition (EEMD) and wavelet threshold was proposed to reduce the noise associated with photoplethysmography signals. In this investigation, this approach was compared with EMD combined with wavelet denoising. Initially, an algorithm applied EEMD to the signal, which was decomposed into a limited number of Intrinsic Mode Functions (IMF). Then, it performed a correlation calculation on the components, followed by wavelet threshold denoising on the noise-containing components. Finally, the signal was reconstructed. The original signal was measured using the stm32 platform with a MAX30100 sensor. The experimental results show that the method can effectively remove high-frequency noise and baseline drift in photoplethysmography. After noise reduction, the signal-to-noise ratio is 34.09 and the root mean square error is 1.99, which improved the signal quality. This new approach facilitates accurate monitoring of photoelectric volume pulse wave signals. © 2019, Science Press. All right reserved.

引用

下载

页码：1327 / 1334

页数：7

共 17 条

[1] Li X., Lu Z.H., Liu X.X., Progress in pulse wave analysis, Beijing Biomedical Engineering, 34, 5, pp. 544-547, (2015)
[2] Li H., Wu Z., Chen Z.C., Et al., Development of heart rate variability measurement system based on android platform, Space Medicine & Medical Engineering, 30, 2, pp. 98-102, (2017)
[3] Liu J., Cai Z.J., Zhang Z.Y., Et al., Evaluation on noninvasive blood components measurement based on photolepthysmography, Opt. Precision Eng., 24, 6, pp. 1264-1271, (2016)
[4] Han Q.Y., Wang X.D., Li B.Y., Et al., Using EEMD to eliminate high frequency noise and baseline drift in pluse blood-oximetry measurement simultaneously, Journal of Electronics & Information Technology, 37, 6, pp. 1384-1388, (2015)
[5] Huang N.E., Shen Z., Long S.R., Et al., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proceedings Mathematical Physical & Engineering Sciences, 454, 1971, pp. 903-995, (1998)
[6] Zhang Q., Ma J., Et al., The effect of EMD modal mixture in wavelet de-noising, Manufacturing Automation, 35, 18, pp. 83-85, (2013)
[7] Rao Y.Z., Wang L.R.R., Et al., A method for blasting vibration signal denoising based on empi cal mode decomposition and wavelet threshold, Journal of Fuzhou University: Natural Science Edition, 43, 2, pp. 271-277, (2015)
[8] He B., Bai Y.P., Zhang Y.T., A study of wavelet soft threshold of ECG denoising based on different empirical mode decomposition, Mathematics in Practice and Theory, 46, 6, pp. 136-144, (2016)
[9] Zhang X., Gong X.F., Speech method of denoising based on EEMD combining energy entropy with wavelet threshold, Computer Engineering and Design, 37, 3, pp. 731-736, (2016)
[10] Mallat S., A theory for multi-resolution vision model applied to astronomical images, IEEE Transactions on Pattern Analysis & Machine Intelligence, 40, pp. 495-520, (1989)

← 1 2 →