基于CEEMDAN-PE的心冲击信号降噪方法研究
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摘要
心冲击信号(BCG)是反应心脏力学特征的生理信号,能实现无电极束缚条件下的连续采集测量,然而BCG信号微弱且极易受到干扰,测量时经常会淹没在噪声中。为了有效识别BCG信号,提出一种基于自适应噪声的完全集合经验模态分解(CEEMDAN)联合排列熵(PE)的BCG降噪方法。首先,将采集到的BCG信号通过CEEMDAN分解得到一系列按频率由高到低的固有模态函数(IMF)。其次,通过PE计算各个IMF分量的值并确定有效信号的阈值范围,从而滤除信号中的高频噪声和基线漂移。最后实验结果显示,降噪后信号的幅频特性得到明显改善且信噪比较传统方法有明显提高,证明了本文降噪方法效果显著,能够有效还原BCG信号特征。
Ballistocardiogram( BCG) signal is a physiological signal that reflects the mechanical characteristics of the heart it can achieve continuous acquisition measurements without electrode binding. However,the BCG signal is weak and highly susceptible to interference,and is often submerged in noise during measurements. In order to effectively identify BCG signals,this paper proposes a BCG de-noising method based on complete ensemble empirical mode decomposition with adaptive noise( CEEMDAN) combined with permutation entropy( PE). Firstly,the collected BCG signal is decomposed with CEEMDAN to obtain a series of intrinsic mode function( IMF) from high to low frequencies. Secondly,the value of each IMF component is calculated with PE and the threshold range of the useful signal is determined,thereby the high frequency noise and baseline drift in the signal are filtered out. The experiment results show that the amplitude-frequency characteristics of the signal after noise reduction are significantly improved,and compared with the traditional method the signal-to-noise ratio is significantly improved,which proves that the proposed noise reduction method has obvious effect and can effectively restore the BCG signal characteristics.
Ballistocardiogram( BCG) signal is a physiological signal that reflects the mechanical characteristics of the heart it can achieve continuous acquisition measurements without electrode binding. However,the BCG signal is weak and highly susceptible to interference,and is often submerged in noise during measurements. In order to effectively identify BCG signals,this paper proposes a BCG de-noising method based on complete ensemble empirical mode decomposition with adaptive noise( CEEMDAN) combined with permutation entropy( PE). Firstly,the collected BCG signal is decomposed with CEEMDAN to obtain a series of intrinsic mode function( IMF) from high to low frequencies. Secondly,the value of each IMF component is calculated with PE and the threshold range of the useful signal is determined,thereby the high frequency noise and baseline drift in the signal are filtered out. The experiment results show that the amplitude-frequency characteristics of the signal after noise reduction are significantly improved,and compared with the traditional method the signal-to-noise ratio is significantly improved,which proves that the proposed noise reduction method has obvious effect and can effectively restore the BCG signal characteristics.
引文
[1] STARR I,SCHROEDER H A. Ballistocardiogram. II.Normal standards, abnormalities commonly found in diseases of the heart and circulation, and their significance[J]. Journal of Clinical Investigation,1940,19(3):437-450.
[2] ALAMETSJ, VRRI A, VIIK J, et al.Ballistocardiogaphic studies with acceleration and electromechanical film sensors[J]. Medical Engineering&Physics,2009,31(9):1154-1165.
[3] INAN O T, MIGEOTTE P F, PARK K S, et al.Ballistocardiography and seismocardiography:A review of recent advances[J]. IEEE Journal of Biomedical and Health Informatics,2015,19(4):1414-1427.
[4] INAN O T,ETEMADI M,WIDROW B,et al. Adaptive cancellation of floor vibrations in standing ballistocardiogram measurements using a seismic sensor as a noise reference[J]. IEEE Transactions on Biomedical Engineering,2010,57(3):722-727.
[5]辛玉,李舜酩,王金瑞,等.基于迭代经验小波变换的齿轮故障诊断方法[J].仪器仪表学报,2018,39(11):79-86.XIN Y,LI SH M,Wang J R,et al. Gear fault diagnosis method based on iterative empirical wavelet transform[J].Chinese Journal of Scientific Instrument,2018,39(11):79-86.
[6] YASLAN Y,BICAN B. Empirical mode decomposition based denoising method with support vector regression for time series prediction:A case study for electricity load forecasting[J]. Measurement,2017(103):52-61.
[7] GACI S. The use of wavelet-based denoising techniques to enhance the first-arrival picking on seismic traces[J].IEEE Transactions on Geoscience and Remote Sensing,2014,52(8):4558-4563.
[8] OMITAOMU O A,PROTOPOPESCU V A,GANGULY A R. Empirical mode decomposition technique with conditional mutual information for denoising operational sensor data[J]. IEEE Sensors Journal,2011,11(10):2565-2575.
[9] BEENAMOL M,PRABAVATHY S,MOHANALIN J.Wavelet based seismic signal de-noising using Shannon and Tsallis entropy[J]. Computers&Mathematics with Applications,2012,64(11):3580-3593.
[10]杨丹,徐彬,叶琳琳,等.心脏心冲击信号降噪方法研究[J].生物医学工程学杂志,2014,31(6):1368-1372.YANG D,XU B,YE L L,et al. De-noising method research of ballistocardiogram signal[J]. Journal of Biomedical Engineering,2014,31(6):1368-1372.
[11] 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 of the Royal Society,1998,454(1971):903-995.
[12] WU Z H, HUANG N. Ensemble empirical mode decomposition:a noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis,2009,1(1):1-41.
[13] COLOMINAS M A,GASTN S,MARA E T. Improved complete ensemble EMD:A suitable tool for biomedical signal processing[J]. Biomedical Signal Processing&Control,2014,14(1):19-29.
[14]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 pulse blood-oximetry measurement simultaneously[J]. Journal of Electronics&Information Technology,2015,37(6):1384-1388.
[15]姜星,耿读艳,张园园,等.基于EMD-ICA的心冲击信号降噪研究[J].中国生物医学工程学报,2019,38(2):138-145.JIANG X,GENG D Y,ZHANG Y Y,et al. BCG signal de-noising method research based on EMD-ICA[J].Chinese Journal of Biomedical Engineering, 2019,38(2):138-145.
[16]田浩辰,赵海文,郭士杰,等.基于振动加速度的无束缚心冲击检测与心率提取方法研究[J].生物医学工程学杂志,2019,36(2):281-290.TIAN H CH, ZHAO H W, GUO SH J, et al.Unconstrained detection of ballistocardiogram and heart rate based on vibration acceleration[J]. Journal of Biomedical Engineering,2019,36(2):281-290.
[17]孙灵芳,王彤彤,徐曼菲,等.基于改进CEEMD的薄层污垢超声检测信号去噪[J].仪器仪表学报,2017,38(12):2879-2887.SUN L F,WANG T T,XU M F,et al. Thin fouling ultrasonic detection signal denoising based on improved CEEMD[J]. Chinese Journal of Scientific Instrument,2017,38(12):2879-2887.
[18]李军,李青.基于CEEMDAN-排列熵和泄漏积分ESN的中期电力负荷预测研究[J].电机与控制学报,2015,19(8):70-80.LI J, LI Q. Medium term electricity load forecasting based on CEEMDAN-permutation entropy and ESN with leaky integrator neurons[J]. Electric Machines and Control,2015,19(8):70-80.
[19] BANDT C,POMPE B. Permutation entropy:A natural complexity measure for time series[J]. Physical Review Letters,2002,88(17):174102.
[20]田秀娟,于德新,邢雪,等.交叉口短时流量CEEMDAN-PE-OSELM预测模型[J].哈尔滨工业大学学报,2018,50(3):83-89.TIAN X J,YU D X,XING X,et al. Prediction model of CEEMDAN-PE-OSELM for intersections short-term traffic flow[J]. Journal of Harbin Institute of Technology,2018,50(3):83-89.
[21]李红,孙冬梅,沈玉成.EEMD降噪与倒频谱分析在风电轴承故障诊断中的应用[J].机床与液压,2018,46(13):156-159.LI H,SUN D M,SHEN Y CH. Application of EEMD noise reduction and cepstrum analysis in fault diagnosis of wind power bearing[J]. Machine Tool&Hydraulics,2018,46(13):156-159.
[2] ALAMETSJ, VRRI A, VIIK J, et al.Ballistocardiogaphic studies with acceleration and electromechanical film sensors[J]. Medical Engineering&Physics,2009,31(9):1154-1165.
[3] INAN O T, MIGEOTTE P F, PARK K S, et al.Ballistocardiography and seismocardiography:A review of recent advances[J]. IEEE Journal of Biomedical and Health Informatics,2015,19(4):1414-1427.
[4] INAN O T,ETEMADI M,WIDROW B,et al. Adaptive cancellation of floor vibrations in standing ballistocardiogram measurements using a seismic sensor as a noise reference[J]. IEEE Transactions on Biomedical Engineering,2010,57(3):722-727.
[5]辛玉,李舜酩,王金瑞,等.基于迭代经验小波变换的齿轮故障诊断方法[J].仪器仪表学报,2018,39(11):79-86.XIN Y,LI SH M,Wang J R,et al. Gear fault diagnosis method based on iterative empirical wavelet transform[J].Chinese Journal of Scientific Instrument,2018,39(11):79-86.
[6] YASLAN Y,BICAN B. Empirical mode decomposition based denoising method with support vector regression for time series prediction:A case study for electricity load forecasting[J]. Measurement,2017(103):52-61.
[7] GACI S. The use of wavelet-based denoising techniques to enhance the first-arrival picking on seismic traces[J].IEEE Transactions on Geoscience and Remote Sensing,2014,52(8):4558-4563.
[8] OMITAOMU O A,PROTOPOPESCU V A,GANGULY A R. Empirical mode decomposition technique with conditional mutual information for denoising operational sensor data[J]. IEEE Sensors Journal,2011,11(10):2565-2575.
[9] BEENAMOL M,PRABAVATHY S,MOHANALIN J.Wavelet based seismic signal de-noising using Shannon and Tsallis entropy[J]. Computers&Mathematics with Applications,2012,64(11):3580-3593.
[10]杨丹,徐彬,叶琳琳,等.心脏心冲击信号降噪方法研究[J].生物医学工程学杂志,2014,31(6):1368-1372.YANG D,XU B,YE L L,et al. De-noising method research of ballistocardiogram signal[J]. Journal of Biomedical Engineering,2014,31(6):1368-1372.
[11] 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 of the Royal Society,1998,454(1971):903-995.
[12] WU Z H, HUANG N. Ensemble empirical mode decomposition:a noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis,2009,1(1):1-41.
[13] COLOMINAS M A,GASTN S,MARA E T. Improved complete ensemble EMD:A suitable tool for biomedical signal processing[J]. Biomedical Signal Processing&Control,2014,14(1):19-29.
[14]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 pulse blood-oximetry measurement simultaneously[J]. Journal of Electronics&Information Technology,2015,37(6):1384-1388.
[15]姜星,耿读艳,张园园,等.基于EMD-ICA的心冲击信号降噪研究[J].中国生物医学工程学报,2019,38(2):138-145.JIANG X,GENG D Y,ZHANG Y Y,et al. BCG signal de-noising method research based on EMD-ICA[J].Chinese Journal of Biomedical Engineering, 2019,38(2):138-145.
[16]田浩辰,赵海文,郭士杰,等.基于振动加速度的无束缚心冲击检测与心率提取方法研究[J].生物医学工程学杂志,2019,36(2):281-290.TIAN H CH, ZHAO H W, GUO SH J, et al.Unconstrained detection of ballistocardiogram and heart rate based on vibration acceleration[J]. Journal of Biomedical Engineering,2019,36(2):281-290.
[17]孙灵芳,王彤彤,徐曼菲,等.基于改进CEEMD的薄层污垢超声检测信号去噪[J].仪器仪表学报,2017,38(12):2879-2887.SUN L F,WANG T T,XU M F,et al. Thin fouling ultrasonic detection signal denoising based on improved CEEMD[J]. Chinese Journal of Scientific Instrument,2017,38(12):2879-2887.
[18]李军,李青.基于CEEMDAN-排列熵和泄漏积分ESN的中期电力负荷预测研究[J].电机与控制学报,2015,19(8):70-80.LI J, LI Q. Medium term electricity load forecasting based on CEEMDAN-permutation entropy and ESN with leaky integrator neurons[J]. Electric Machines and Control,2015,19(8):70-80.
[19] BANDT C,POMPE B. Permutation entropy:A natural complexity measure for time series[J]. Physical Review Letters,2002,88(17):174102.
[20]田秀娟,于德新,邢雪,等.交叉口短时流量CEEMDAN-PE-OSELM预测模型[J].哈尔滨工业大学学报,2018,50(3):83-89.TIAN X J,YU D X,XING X,et al. Prediction model of CEEMDAN-PE-OSELM for intersections short-term traffic flow[J]. Journal of Harbin Institute of Technology,2018,50(3):83-89.
[21]李红,孙冬梅,沈玉成.EEMD降噪与倒频谱分析在风电轴承故障诊断中的应用[J].机床与液压,2018,46(13):156-159.LI H,SUN D M,SHEN Y CH. Application of EEMD noise reduction and cepstrum analysis in fault diagnosis of wind power bearing[J]. Machine Tool&Hydraulics,2018,46(13):156-159.
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