基于平滑先验法去除脉搏波基线漂移

脉搏波信号中含有丰富的人体生理病理信息,然而基于光电容积描记法采集到的脉搏波基线漂移非常严重,直接影响到人体生理参数的准确提取。针对目前生物信号处理领域中去除基线漂移所用的方法计算复杂,处理信号时间过长,无法满足对脉搏波进行实时处理等问题,首次提出基于正则化最小二乘法的平滑先验法去除脉搏波的基线漂移。该方法通过分析不同正则化参数下平滑先验法的截止频率,结合脉搏波信号中基线漂移信号的频率范围,选取合适的正则化参数,实现脉搏波基线漂移的去除。实验结果表明,与小波变换法、经验模态分解法相比,该方法去除脉搏波基线漂移效果明显,提高了计算速度,同时也提高了信噪比,有利于下一步对脉搏波特征点的精确提取。     

结合QRS检测和小波阈值的膈肌肌电信号降噪方法

为了消去夹杂在膈肌肌电(EMGdi)信号中的心电干扰,在比例阈值算法的基础上,提出一种结合QRS检测和小波阈值的降噪方法.首先,根据小波系数的相关性构造QRS波群的检测方法,分析确定干扰的位置和范围;其次,将小波系数分为受干扰和未受干扰两部分,并构造相应的阈值算法,针对性地处理受干扰系数,以未受干扰部分系数作为阈值算法构造的依据;最后,重构处理后的小波系数,得到降噪后的EMGdi信号.对临床采集信号的处理对比表明,该方法能够更为有效地去除心电干扰,并更好地保留EMGdi的有用信号.     

心电信号小波去噪的改进算法研究

对心电信号(ECG)中的基线漂移、工频干扰和肌电干扰等噪声进行去除,在波形识别、医疗诊断和治疗等领域具有重要意义。提出用sym5小波函数对心电信号进行8层小波分解。根据有用信号强度在每一层平均分配而噪声强度随分解层数增加而减少的规律,将分解得到的每一层的小波细节系数设置不同的阈值,最后用所提出的新阈值函数进行小波阈值去噪。该阈值函数既能克服硬阈值函数在阈值附近不连续的缺点,又可弥补软阈值函数与原函数之间存在固定差值的不足。以MIT-BIH心电数据库中的101号文件作为原始数据,将整个数据文件进行平均分段,每段有1 200个数据点,对每段数据进行加噪仿真分析,结果表明所提出的去噪算法得到的去噪信号信噪比比硬阈值函数和软阈值函数分别提高2.31%和8.04%,从而证明所提算法的有效性。     

一种基于提升小波和中值滤波的心电去噪方法

小波变换在心电去噪中有非常好的效果,但传统的小波变换计算量大,不利于实时处理和嵌入式系统的实现,提升小波是一种快速有效的小波变换的实现方法,本文提出了一种运用提升小波和中值滤波去除心电信号工频干扰、肌电干扰和基线漂移三种噪声的方法。该方法运用提升小波对含噪声的心电信号做三层分解,并根据小波基的特性在不同层次采用不同的小波基,去除心电信号的工频干扰和肌电干扰;对第三层分解后得到的数据做中值滤波,去除心电信号的基线漂移。将以上方法与传统的小波方法相比,去噪结果表明两者去噪效果相当,但提升方法运算速度有很大的提升。结果证实将提升小波与中值滤波方法结合可以有效地去除心电信号的工频干扰、肌电干扰和基线漂移,而且可以较大地提高运算速度,便于进行实时处理和嵌入式系统的实现。     

基于小波变换的充气法测量血压的算法研究

目的探索腕部充气测量血压的方法及原理,实现小波变换提取脉搏波以及无创血压的计算,为现有腕式电子血压计提供更精确的算法。方法采用小波变换对采集的袖带脉搏压力混合信号进行去噪处理,并分离脉搏波与袖带压。在此基础上采用差分法与阈值法寻找脉搏波峰值点并修正波形干扰点,再对脉搏波峰值点进行高斯曲线拟合法拟合出平滑包络线,并采用改进的幅度系数法进行收缩压、舒张压的计算,并对30名测试者用本方法与听诊法进行对比测试,观察相关性。结果该方法的测试结果与听诊法对比相关性良好,测量血压速度快,舒适度高。结论基于小波变换的充气法测量血压的算法相比传统方法去噪效果好,脉搏波提取精确度高,血压计算结果符合AAMI标准,但需在软件处理算法中做进一步简化研究。     

基于小波分解的颈动脉波特征提取算法

目的:利用小波变换的时频局域化性质,检测出存在于颈动脉波信号(CAP)中的奇异点和奇异角,并且精确检测奇异角出现的位置。方法:小波变换具有多分辨率等特点,能够通过放大信号的任意细节部分进行时域分析。采用离散小波变换法结合db1小波能够检出脉搏信号中的奇异U角。利用计算CAP时域特征点的小波变换极大值坐标来精确定位脉搏时域特征点,通过检测脉搏的特征参数以及脉搏的突变特征参数,可以客观判定人体脉搏变化规律。结果:CAP信号WT分解很好地抑制了各种病理性、基线漂移等干扰,为进一步进行特征提取创造了条件,基于第一细节信号d1的特征点定位几乎不受各种病理性、基线漂移等干扰的影响,定位比其他传统处理技术更为准确。结论:本文提出了基于小波分解的颈动脉波特征点提取算法,取得高达100%的检测率。在含有大量噪声和伪差的脉搏信号中,仍具有较高正确检出率和良好的抗噪性。根据计算得到CAP信号时域特征点的小波变换极大值的坐标,再利用极大值表征准确测定脉象时域特征点的坐标,能够克服脉搏时域特征点定位不准的问题。     

基于脉搏波传播时间测量的特征信号处理算法的研究

目的针对目前加速度脉搏波特征点检测研究少的问题,本文提出一系列处理算法,并对加速度脉搏波关键点中的a、c两点进行了重点研究。方法通过对加速度脉搏波采用防脉冲移动平均、小波滤波等方法预处理后,采取差分阈值与小波系数模极大值相结合的方法,对关键点位置进行检测。实验处理数据源于对15名在校学生采集的60组容积脉搏波,通过本文算法进行处理、检测和验证。结果对于加速度脉搏波中关键点a、c两点的正确识别率达92％以上。结论本文所述信号处理算法能够对脉搏波传播时间测量中的特征信号进行快速、准确的检出,为新型医疗监护设备的开发设计提供了技术支持。     

基于小波变换和改进的瞬态独立成分分析融合算法的心电信号降噪方法

本文提出一种基于小波变换与独立成分分析融合的信号处理方法,该方法用于抑制多通道同步采集的心电信号包含的噪声。首先利用小波变换对各路同步采集的原始心电信号进行八尺度分解,获得低频逼近信号与高频细节信号,通过设定阈值的方法去除属于低频噪声部分的逼近信号。然后对保留的细节信号进行反变换实现信号重构,再利用包含预同步功能的瞬态独立成分分析改进算法从重构的信号中分离出高频噪声与心电信号独立成分。最后采用信噪比与均方根误差作为信号质量评价指标,将融合算法与单独使用瞬态独立成分分析算法的处理结果进行对比,结果表明融合算法进行降噪处理这一方法具有更高的信噪比和更低的均方根误差,本文提出的融合算法具有良好的心电信号降噪性能。     

基于小波变换的改进阈值法在心电信号去噪中的应用

目的为了克服传统小波硬阈值函数的不连续性和软阈值函数有偏差的缺点,并消除采用固定阈值带来的偏差。方法改变小波阈值函数部分参数得到一种新的小波阈值函数,新的小波阈值函数可以看成是硬阈值函数和软阈值函数的线性组合,既克服了软硬阈值函数的缺点,同时又能在它们之间进行灵活选择,小波阈值的选取采用了一种自适应阈值。结果改进阈值法能有效去除心电信号中常见的工频干扰、肌电干扰和基线漂移3种噪声。结论与软硬阈值法相比,无论是从视觉效果上还是信噪比上都有较大的改善。     

脉搏血氧饱和度检测中自适应滤波消除运动伪差的方法研究

目的研究脉搏血氧饱和度检测系统中运动伪差的消除方法,以提高脉搏血氧仪检测性能。方法通过脉搏血氧仪中的双光束构造噪声参考信号,利用最小均方自适应滤波法消除运动伪差干扰的影响。结果建立了脉搏血氧饱和度检测中消除运动伪差的计算方法,可成功地从运动伪差中提取正常光电容积脉搏波信号作为计算氧饱和度的依据。结论该计算方法简单,可用于实时处理,且测量结果可靠,为进一步抑制脉搏血氧仪噪声奠定了基础。     

EMD-based Adaptive Wavelet Threshold for Pulse Wave Denoising

It is inevitable that noises will be introduced during the acquisition of pulse wave signal, which can result in morphology changes of the original pulse wave,and affect the hemodynamic analysis and diagnosis based on pulse wave signals. In order to remove these noises, an adaptive de-noising method based on empirical mode decomposition(EMD) and wavelet threshold is proposed in this paper. Compared with the wavelet threshold method for denoising pulse wave, the proposed approach is more effective, especially at low signal-to-noise ratio.     

Research on the Method of EEMD in Pulse Wave Signals Processing

The mixed noise in the acquisition process of pulse wave signals will affect the signal analysis, how to effectively eliminate the noise and complete the pulse wave analysis has important practical significance. In this paper, empirical mode decomposition(EMD) and ensemble empirical mode decomposition(EEMD) were used to realize scale decomposition of pulse wave signals to obtain intrinsic mode function(IMF). A band-pass filter was implemented according to the characteristic time scale parameters of the IMF. After filtering and reconstruction, the pulse wave denoising was completed. The denoising effects of EMD, EEMD and wavelet transform were compared in terms of mean square error and signal-to-noise ratio. The result shows that EMD and EEMD are better than wavelet transform, and the effects are similar. Further comparing the Hilbert-Huang spectrum of EMD and EEMD, it can be seen that EEMD can not only avoid mode mixing, but also facilitate the analysis of pulse wave signals.     

Design of a wavelet interpolation filter for enhancement of the ST-segment

A wavelet interpolation filter (WIF) is designed for the removal of motion artifacts in the ST-segment of stress ECGs. The WIF consists of two parts. One part is a wavelet transform that decomposes the stress ECG signal into several frequency bands using a Haar wavelet. The other part is an interpolation method, such as the spline technique, that is used to enhance the reconstruction performance of the signal decomposed by the wavelet transform. To evaluate the performance of the WIF, three indices are used: signal-to-noise ratio (SNR), reconstruction square error (RSE) and standard deviation (SD). The MIT/BIH arrhythmia database, the European ST-T database and the triangular wave are used for evaluation. A noisy ECG signal, corrupted by motion artifacts, is simulated by the addition of two types of random noise to the original ECG signal. For comparison, three indices for the other methods are also computed: mean, median and hard thresholding. The performance of the WIF shows that RSE, SNR and SD are 392.7, 18.3 dB and 2.6, respectively, in the case of a noisy signal with an SNR of 7.1 dB. This result is much better than those for the other methods.     

一种同时消除脉搏波信号中呼吸基线漂移和高频噪声的方法

目的：基于光电容积脉搏波可以实现血氧饱和度等人体生理参数的无创检测。基于光电容积脉搏波测量时,由于信号采集过程中存在人体呼吸和仪器本身热噪声等干扰,脉搏波信号中存在着呼吸基线漂移和高频噪声,影响最终的人体生理参数测量精度。方法：因此提出一种在经验模式分解的过程中结合小波变换的方法,来同时消除呼吸基线漂移和高频噪声的影响。首先通过经验模态分解将脉搏波信号分解为若干内在模式分量,并分别判断出含有呼吸基线漂移和代表高频噪声的分量,对于代表高频噪声的分量采用类似小波变换的方法进行滤波,利用小波变换将含有呼吸基线漂移的分量分解,将代表呼吸基线漂移的小波细节置零,信号重构后就达到了同时消除呼吸基线和高频噪声的目的。利用自行研制的测量装置采集的脉搏波信号进行实验验证,并采用信号交直流比R和信号的频谱进行效果评价。结果：有效地同时消除了呼吸基线漂移和高频噪声。结论：该方法将有利于血氧饱和度等人体生理参数无创检测精度的提高。     

Spectral trimming technique: a new approach for suppressing motion artefacts in stress electrocardiography

Abstract

Motion artefacts in electrocardiographic (ECG) signal are suppressed mainly by adaptive noise cancellation and wavelet denoising. While the former requires a motion sensor in addition to ECG electrodes, the latter removes some of the desired low-frequency components in the signal. In this paper spectral trimming technique is being introduced for suppressing the motion artefacts in stress electrocardiography. In this method, Fourier spectral coefficients up to 1.221?Hz of noisy signal are trimmed on the basis of template derived from resting ECG signal in the same subject. The proposed spectral trimming technique has yielded the lowest value of mean?±?standard deviation for root mean square error (18.92?±?8.71) and highest value of the signal to noise ratio (6.439?±?4.266) as compared to other three methods, namely adaptive noise cancellation, wavelet decomposition and adaptive line enhancement with compatible value of correlation coefficient. Subsequently, spectral trimming technique has been implemented in real-time (deferred by 8.2?s) application for stress electrocardiography. Spectral trimming technique thus offers a method of choice for motion artefact suppression in offline as well as deferred online applications. This method takes care of the limitations of conventional methods such as adaptive noise cancellation or wavelet denoising for suppressing motion artefacts in stress electrocardiography.     

轮廓波及曲波和小波变换用于显微图像消噪的比较

背景：小波变换只能反映信号的零维奇异性,无法最优表示图像中的线奇异;而且小波变换只存在3个方向,这些都显著影响了它在图像处理领域的应用效果。针对小波变换的缺点,多尺度几何分析理论正在逐步发展,轮廓波变换和曲波变换就是其中的典型代表。 目的：定性、定量地比较轮廓波、曲波和小波变换在图像消噪处理中的效果。 方法：在简要介绍3种变换基本原理的基础上,比较它们在图像消噪领域的应用,以均方误差和峰值信噪比作为定量指标评价消噪效果,并将其应用于显微镜图像的消噪处理。 结果与结论：综合定量评价指标和人眼视觉感受,曲波变换的消噪结果最佳,轮廓波变换效果次之,小波变换效果则不够理想。     

双通道表面肌电信号采集装置的设计与分析

本研究设计了一种双通道的表面肌电信号(surface electromyography,sEMG)采集装置。该装置以STM32为主控芯片,配以sEMG采集模块,实现对肌电信号的采集,并将数据传至由MATLAB编程的上位机进行分析处理。该装置对于人体内部及周围环境干扰噪声的处理均在硬件上实现,信噪比约为60~70 dB。将本装置与Noraxon-DTS系列无线sEMG采集装置采集的肌电信号进行比对,结果表明,该装置能更好地滤除50 Hz的工频干扰;当受试者做屈肘动作时,肱二头肌的频谱信号与Noraxon装置的测试结果一致,表明该装置能高精度地采集sEMG,且具有很强的抗干扰能力。     

Estimation of Trial-to-Trial Variation in Evoked Potential Signals by Smoothing Across Trials

Averaging single trial evoked potential data to produce an estimate of the underlying signal obscures trial-to-trial variation in the response. We describe a method for estimating slow changes in the evoked potential signal by smoothing the data over trials. We discuss the crucial issue of deciding how much to smooth and suggest that an appropriate smoothing parameter is one that minimizes the estimated mean average square error of the smoothed data. Equations to estimate the mean average square error for a one-dimensional local linear regression smoother are presented. Performance of the method is assessed using simulated evoked potential data with several different models of a changing signal and different values of the signal-to-noise ratio. We find that the method rarely imputes trial-to-trial variation to data sets that have an unchanging signal, while it almost always produces less error than averaging when estimating a varying signal. The ability of the method to reveal signal heterogeneity is hampered by very low signal-to-noise ratios. When applied to real auditory evoked potential data from a sample of elderly subjects, the method indicated a changing signal in 35% of all subjects and in 56% of subjects with signal-to-noise ratios above 0.6. Consistent patterns of variation in the auditory evoked potential were present in this sample.     

Comparative study of wavelet denoising in myoelectric control applications

Here, the wavelet analysis has been investigated to improve the quality of myoelectric signal before use in prosthetic design. Effective Surface Electromyogram (SEMG) signals were estimated by first decomposing the obtained signal using wavelet transform and then analysing the decomposed coefficients by threshold methods. With the appropriate choice of wavelet, it is possible to reduce interference noise effectively in the SEMG signal. However, the most effective wavelet for SEMG denoising is chosen by calculating the root mean square value and signal power values. The combined results of root mean square value and signal power shows that wavelet db4 performs the best denoising among the wavelets. Furthermore, time domain and frequency domain methods were applied for SEMG signal analysis to investigate the effect of muscle-force contraction on the signal. It was found that, during sustained contractions, the mean frequency (MNF) and median frequency (MDF) increase as muscle force levels increase.