基于改进互补集成经验模态分解的脉搏波去噪  

Pulse Wave Denoising Based on Improved Complementary Ensemble Empirical Mode Decomposition

作　　者：陈勇[1] 姚知民 刘焕淋[2] 廖钧鹏 许力 冯彦清 Chen Yong;Yao Zhimin;Liu Huanlin;Liao Junpeng;Xu Li;Feng Yanqing(Key Laboratory of Industrial Internet of Things&Network Control,Ministry of Education,Chongqing University of Posts and Telecommunications,Chongqing 400065,China;School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)

机构地区：[1]重庆邮电大学工业物联网与网络化教育部重点实验室,重庆400065 [2]重庆邮电大学通信与信息工程学院,重庆400065

出　　处：《光学学报》2024年第7期51-60,共10页Acta Optica Sinica

基　　金：国家自然科学基金(51977021)。

摘　　要：针对脉搏波信号采集过程中存在噪声的问题,提出了基于改进互补集成经验模态分解的脉搏波去噪算法。利用光纤布拉格光栅传感器获取脉搏波信号,首先在互补集成经验模态分解算法中加入高斯白噪声,然后利用粒子群算法优化高斯白噪声幅值,以此来消除互补集成经验模态算法分解产生的模态混叠现象,并联合小波阈值函数对其处理后的脉搏波信号进行重构。实验结果表明,所提算法能够有效降低脉搏波信号中的噪声干扰,在信噪比、均方误差两个指标上均优于对比算法,为提取脉搏波的时域特征奠定了基础。Objective The cardiovascular health status of the human body can be reflected through pulse waves.Important physiological parameters such as heart rate,blood pressure,and the degree of vascular sclerosis can be obtained through the analysis of these waves.The sensor predominantly used for pulse measurement is the photoelectric sensor,which is capable of detecting pulses at various measurement positions,thus making it extensively used in wearable sports equipment for heart rate detection.However,during the process of measuring pulse waves with photoelectric sensors,there are often various noise interferences such as motion artifacts,power interference,and respiratory effects.Moreover,this measurement method is primarily invasive,which can make people uncomfortable.Therefore,it is necessary to select appropriate sensors to avoid discomfort to the human body during the measurement process and denoise the collected signals.Methods We designed a pulse wave signal acquisition platform based on fiber Bragg grating(FBG)sensors.The platform was composed of FBG sensors embedded in nylon wristbands.Initially,the FBG wristband was secured at the radial artery of the left hand to gather pulse wave signals for demodulation.The collected pulse wave signals were subject to baseline drift.Hence,integrated empirical mode decomposition(EMD)and cubic spline interpolation were used for detrending prior to denoising.Subsequently,the amplitude of Gaussian white noise added to the complementary ensemble EMD(CEMMD)was optimized using particle swarm optimization(PSO)algorithm.The CEEMD algorithm decomposed the pulse wave signal into a series of intrinsic mode function(IMF)components.An improved wavelet threshold function was then applied to process these IMF components.The correlation coefficient between each IMF component and the original pulse wave signal was calculated,and this coefficient was used to determine the effectiveness of each component.Finally,all effective signals were reconstructed to obtain a smooth pulse wave signal.Resu

关 键 词：光纤布拉格光栅 脉搏波 信号去噪 互补集成经验模态分解 粒子群优化算法 小波阈值 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置]