EEMD结合小波阈值的光电容积脉搏波信号降噪

陈真诚; 吴贤亮; 赵飞骏

doi:10.3788/OPE.20192706.1327

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EEMD结合小波阈值的光电容积脉搏波信号降噪

生命科学仪器 | 更新时间：2020-08-13

- EEMD结合小波阈值的光电容积脉搏波信号降噪
- Denoising and implementation of a photoplethysmography signal based on EEMD and wavelet threshold
- 光学精密工程 2019年27卷第6期页码：1327-1334
- 作者机构：
  
  1.桂林电子科技大学电子工程与自动化学院，广西桂林 541004
  2.桂林电子科技大学生命与环境科学学院，广西桂林 541004
- 作者简介：
  
  [ "陈真诚(1965-)，男，湖南永州人，教授，博士生导师，1988年于北京军医学院获得学士学位，1999年于西安电子科技大学获得硕士学位，2002年于西安交通大学获得博士学位，主要从事生物传感与智能仪器的研究。E-mail：chenzhcheng@163.com" ]
  [ "吴贤亮(1992-)，男，吉林长春人，硕士研究生，2015年于长春理工大学获得学士学位，主要从事生物传感与智能仪器的研究。E-mail：564672455@qq.com" ]
- 基金信息：
  
  国家自然科学基金资助项目(61627807);国家自然科学基金资助项目(81873913);GUET优秀毕业论文计划资助项目(16YJPYBS03);桂林电子科技大学研究生留学项目资助;广西自然科学基金资助项目(2015GXNSFAA139032);广西自然科学基金资助项目(2016GXNSFBA380145);广西自然科学基金资助项目(2017GXNSFGA198005)
- DOI：10.3788/OPE.20192706.1327
  中图分类号： TH133.33
- 收稿日期：2018-11-16，
  
  录用日期：2018-12-25，
  
  纸质出版日期：2019-06-15
- 稿件说明：
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陈真诚, 吴贤亮, 赵飞骏. EEMD结合小波阈值的光电容积脉搏波信号降噪[J]. 光学精密工程, 2019,27(6):1327-1334.

Zhen-cheng CHEN, Xian-liang WU, Fei-jun ZHAO. Denoising and implementation of a photoplethysmography signal based on EEMD and wavelet threshold[J]. Optics and precision engineering, 2019, 27(6): 1327-1334.
陈真诚, 吴贤亮, 赵飞骏. EEMD结合小波阈值的光电容积脉搏波信号降噪[J]. 光学精密工程, 2019,27(6):1327-1334. DOI： 10.3788/OPE.20192706.1327.

Zhen-cheng CHEN, Xian-liang WU, Fei-jun ZHAO. Denoising and implementation of a photoplethysmography signal based on EEMD and wavelet threshold[J]. Optics and precision engineering, 2019, 27(6): 1327-1334. DOI： 10.3788/OPE.20192706.1327.

摘要

为了研究脉搏波信号降噪的问题，文章提出了一种集合经验模态分解(Ensemble Empirical Mode Decomposition，EEMD)与小波阈值相结合的降噪方法，对采集到的光电容积脉搏波信号来做降噪处理，同时和EMD结合小波阈值降噪算法进行比较。算法首先把信号做EEMD的分解，将原始信号分解为

个模态分量(Intrinsic Mode Function，IMF)，然后对这些分量做相干性的计算，对其中的噪声分量来做小波阈值降噪，最后将信号重构。原始信号在STM32平台上采用MAX30100传感器测得。实验结果表明：本文的方法能够很好地剔除光电容积脉搏波中包含高频噪声与基线漂移的各种噪声，降噪后信噪比为34.09，均方根误差为1.99。提高了PPG信号的质量，为光电容积脉搏波信号的准确测量提供了新的思路。

Abstract

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.

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references

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