EEMD结合小波阈值的光电容积脉搏波信号降噪
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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n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。
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.
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.
引文
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[15]梁海清.中低速磁浮交通限界研究[D].成都:西南交通大学,2008.LIANG H Q.Research on the Limitation of Medium and Low Speed Maglev Transportation[D].Chengdu:Southwest Jiaotong University,2008.(in Chinese)
[16]陈仁祥,汤宝平,马婧华.基于EEMD的振动信号自适应降噪方法[J].振动与冲击,2012,31(15):82-86.CHEN R X,TANG B P,MA J H.Adaptive noise reduction method for vibration signal based on EEMD[J].Journal of Vibration and Shock,2012,31(15):82-86.(in Chinese)
[17]张雪英,谢飞,乔铁柱,等.基于EEMD与改进小波阈值的磁记忆信号降噪研究[J].太原理工大学学报,2015,46(5):592-597.ZHANG X Y,XIE F,QIAO T ZH,et al..Research on magnetic memory signal denoising based on EEMD and improved wavelet threshold[J].Journal of Taiyuan University of Technology,2015,46(5):592-597.(in Chinese)
[2]李浩,吴综,陈真诚,等.基于Android平台的心率变异性检测系统[J].航天医学与医学工程,2017,30(2):98-102.LI H,WU Z,CHEN ZH CH,et al..Development of heart rate variability measurement system based on android platform[J].Space Medicine &Medical Engineering,2017,30(2):98-102.(in Chinese)
[3]刘瑾,蔡子晋,张紫杨,等.光电容积脉搏波无创测量人体血液成分的评估[J].光学精密工程,2016,24(6):1264-1271.LIU J,CAI Z J,ZHANG Z Y,et al..Evaluation on noninvasive blood components measurement based on photolepthysmography[J].Opt.Precision Eng.,2016,24(6):1264-1271.(in Chinese)
[4]韩庆阳,王晓东,李丙玉,等.EEMD在同时消除脉搏血氧检测中脉搏波信号高频噪声和基线漂移中的应用[J].电子与信息学报,2015,37(6):1384-1388.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[J].Journal of Electronics &Information Technology,2015,37(6):1384-1388.(in Chinese)
[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[J].Proceedings Mathematical Physical &Engineering Sciences,1998,454(1971):903-995.
[6]张倩,马钺.EMD模态混叠问题在小波去噪中的影响[J].制造业自动化,2013,35(18):83-85.ZHANG Q,MA J,et al..The effect of EMD modal mixture in wavelet de-noising[J].Manufacturing Automation,2013,35(18):83-85.(in Chinese)
[7]饶运章,王柳,饶睿,等.基于EMD与小波阈值的爆破震动信号去噪方法[J].福州大学学报:自然科学版,2015,43(2):271-277.RAO Y ZH,WANG L,R R,et al..A method for blasting vibration signal denoising based on empi cal mode decomposition and wavelet threshold[J].Journal of Fuzhou University:Natural Science Edition,2015,43(2):271-277.(in Chinese)
[8]赫彬,白艳萍,张雅婷.基于不同经验模态分解的小波软阈值ECG去噪研究[J].数学的实践与认识,2016,46(6):136-144.HE B,BAI Y P,ZHANG Y T.A study of wavelet soft threshold of ECG denoising based on different empirical mode decomposition[J].Mathematics in Practice and Theory,2016,46(6):136-144.(in Chinese)
[9]张雪,龚晓峰.EEMD联合能量熵及小波阈值的语音去噪方法[J].计算机工程与设计,2016,37(3):731-736.ZHANG X,GONG X F.Speech method of denoising based on EEMD combining energy entropy with wavelet threshold[J].Computer Engineering and Design,2016,37(3):731-736.(in Chinese)
[10]MALLAT S.A theory for multi-resolution vision model applied to astronomical images[J].IEEE Transactions on Pattern Analysis &Machine Intelligence,1989,40:495-520.
[11]曹伟,孙红梅,贾瑞生,等.基于小波包分解重构的微地震信号降噪方法[J].电子测量与仪器学报,2018,32(4):134-143.CAO W,SUN H M,JIA R SH,et al..Microseismic signal denoising method based on wavelet packet decomposition and reconstruction[J].Journal of Electronic Measurement and Instrument,2018,32(4):134-143.(in Chinese)
[12]AWAL M A,MOSTAFA S S,AHMAD M,et al..An adaptive level dependent wavelet thresholding for ECG denoising[J].Biocybernetics &Biomedical Engineering,2014,34(4):238-249.
[13]任志英,高诚辉,罗德海,等.二维经验模态分解在工程表面形貌误差评定中的应用[J].光学精密工程,2017,25(2):425-432.REN ZH Y,GAO CH H,LUO D H,et al..Application of bidimensional empirical mode decomposition in error evaluation for engineering surface texture[J].Opt.Precision Eng.,2017,25(2):425-432.(in Chinese)
[14]ZHAOHUA WU,NORDEN E HUANG.Ensemble empirical mode decomposition:a noise-assisted data analysis method[J].Advances in Adaptive Data Analysis,2011,1(1):1-41.
[15]梁海清.中低速磁浮交通限界研究[D].成都:西南交通大学,2008.LIANG H Q.Research on the Limitation of Medium and Low Speed Maglev Transportation[D].Chengdu:Southwest Jiaotong University,2008.(in Chinese)
[16]陈仁祥,汤宝平,马婧华.基于EEMD的振动信号自适应降噪方法[J].振动与冲击,2012,31(15):82-86.CHEN R X,TANG B P,MA J H.Adaptive noise reduction method for vibration signal based on EEMD[J].Journal of Vibration and Shock,2012,31(15):82-86.(in Chinese)
[17]张雪英,谢飞,乔铁柱,等.基于EEMD与改进小波阈值的磁记忆信号降噪研究[J].太原理工大学学报,2015,46(5):592-597.ZHANG X Y,XIE F,QIAO T ZH,et al..Research on magnetic memory signal denoising based on EEMD and improved wavelet threshold[J].Journal of Taiyuan University of Technology,2015,46(5):592-597.(in Chinese)