基于EMD与IMF分量统计特性的ECG去噪

经验模式分解（EMD）域内心电（ECG）信号的去噪,通常为基于QRS特征波经验性识别固有模态函数（IMF）分量并重建ECG信号。由于该方法引入个人误差,因此识别不准确。针对此问题,本文提出利用EMD与IMF分量统计特性对ECG信号进行去噪。本方法首先对含噪ECG信号进行EMD分解得到一系列IMF分量,然后利用IMF分量的统计特性识别IMF分量属性,并采用被识别为ECG信号的IMF分量重建ECG信号。该识别方法基于统计学方法,具有统计学和现实物理意义。将本方法应用于真实ECG信号去噪处理中,结果表明,本方法可有效去除ECG信号基线漂移噪声与肌电干扰噪声,去噪效果优于经验法。     

Selection of mother wavelet and denoising algorithm for analysis of foetal phonocardiographic signals

This paper is aimed at the selection of suitable mother wavelet and denoising algorithm for the analysis of foetal phonocardiographic (fPCG) signals. Fourier based analysing tools have some limitations concerning frequency and time resolutions. Although wavelet transform (WT) overcomes these limitations, it requires proper selection of a mother wavelet and denoising algorithm. In this study a suitable mother wavelet is selected on the basis of properties of different wavelet families and characteristics of the fPCG signals. The universal threshold, minimax threshold and rigorous SURE threshold algorithms along with soft or hard thresholding rule have been compared to denoise these signals. The mean squared error (MSE) is used to evaluate the performance of these algorithms. The results show that the fourth order Coiflets wavelet has a better performance for the analysis of fPCG signals when using the rigorous SURE threshold denoising algorithm with soft thresholding rule. The proposed approach is simple and proves to be effective when applied to the selection of suitable mother wavelet and denoising algorithm for the fPCG signals. These denoised signals can be used for the accurate determination of foetal heart rate (FHR) and further diagnostic applications of the foetus.     

基于Hilbert-Huang变换的医学超声信号去噪

目的:探求一种基于Hilbert-Huang变换的医学超声信号去噪方法。方法:提出了一种基于Hilbert-Huang变换的医学超声信号去噪方法。首先对含噪超声信号进行经验模式分解,得到各阶IMF分量,然后对高频的IMF分量用阈值方法进行处理,把经过阈值处理的高频的IMF分量和低频IMF分量进行叠加,得到重构的去噪信号。结果:仿真实验表明,基于Hilbert-Huang变换的医学超声信号去噪方法可以有效地降噪。结论:Hilbert-Huang变换的医学超声信号去噪方法在自适应性和先验性方面优于基于小波的去噪方法。     

基于小波变换的脉搏波信号去噪

人体脉象信号是一种信噪比较低的非平稳随机信号,在分析脉象信号之前去噪是一项十分重要的工作。针对小波变换中的阈值法进行公式上的改进,并利用ZM—ⅢC型智能化中医脉象仪采集到的亚健康人群左关外桡动脉脉搏信号进行去噪处理,实验结果表明,改进后的阈值法可以取得更好的去噪效果。     

基于小波变换的脉搏信号分析仪的设计

脉搏信号中含有丰富的人体生理信息,对心血管疾病的预防和诊治有着重要的指导作用。本文采用COM组件技术将基于小波变换的脉搏信号去噪和特征提取MATLAB算法程序无缝集成到LabVIEW中,实现了虚拟脉搏信号分析仪的设计。实验结果证明该分析仪采用的自适应阈值小波消噪方法的消噪效果优于传统的软、硬阈值法,提取的脉搏信号各尺度能量值可以用来作为区分心血管疾病患者和正常人群的特征值,扩展的网络传输功能经实际应用具有非常实用的价值。     

最优改进对数谱幅度估计与小波结合的心音降噪

小波阈值降噪为心音降噪的一种常用方法。本文提出了使用最优改进对数幅度谱估计与小波阈值降噪相结合的方法对心音降噪。在正常心音和一些常见疾病的心音中加入不同强度的白噪声和粉红噪声，构造不同信噪比的心音信号，并将本文所提出的方法和仅用小波阈值降噪方法的去噪效果进行了定量的对比。结果表明本文方法降噪效果总体优于仅使用小波阈值降噪达到的效果。     

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.     

基于小波阈值法的脉搏波去噪算法

目的消除可穿戴式脉搏波监测设备在连续测量中由于运动造成的运动伪差,保证设备准确性和稳定性。方法通过选取合适的小波基、小波最大分解层数、阈值函数和阈值方法,对脉搏波信号进行小波阈值处理,提出了一种基于小波阈值法去除脉搏波噪声的算法。并针对在脉搏波信号采集过程中出现的基线漂移、工频干扰和运动伪差,与加窗傅里叶变换去噪后的结果进行对比。结果在信噪比、均方差和平滑度等关键指标上,小波阈值法的效果更优。利用db9小波基对脉搏波信号进行6层小波分解,设置启发式阈值所得到的处理效果最好。结论该算法能够有效抑制工频干扰和运动干扰,使信噪比提高22 dB,均方差接近于0,且平滑度降为原来的11%,实现脉搏波信号采集中干扰的有效去除。     

Filtering of surface EMG using ensemble empirical mode decomposition

Surface electromyogram (EMG) is often corrupted by three types of noises, i.e. power line interference (PLI), white Gaussian noise (WGN), and baseline wandering (BW). A novel framework based primarily on empirical mode decomposition (EMD) was developed to reduce all the three noise contaminations from surface EMG. In addition to regular EMD, the ensemble EMD (EEMD) was also examined for surface EMG denoising. The advantages of the EMD based methods were demonstrated by comparing them with the traditional digital filters, using signals derived from our routine electrode array surface EMG recordings. The experimental results demonstrated that the EMD based methods achieved better performance than the conventional digital filters, especially when the signal to noise ratio of the processed signal was low. Among all the examined methods, the EEMD based approach achieved the best surface EMG denoising performance.     

联合主成分分析-小波与整体平均经验模态分解的锋电位去噪方法

多通道微电极阵列记录的锋电位(Spike)十分微弱,极易受干扰,其含噪的特性影响了Spike检出的准确率。针对Spike检测过程中通常存在的独立白噪声、相关噪声与有色噪声,本文结合主成分分析(PCA)、小波分析和自适应时频分析,提出PCA-小波(PCAW)与整体平均经验模态分解(EEMD)联合的去噪新方法(PCWE)。首先,利用PCA提取多通道神经信号通道间的主成分作为相关噪声去除;然后利用小波阈值法对独立白噪声进行去除;最后利用EEMD把噪声分解到各层本质模态函数中,对有色噪声进行去除。仿真结果表明,PCWE使信噪比约提高2.67 dB,标准差约减小0.4μV,显著提高了Spike的检出精确率;实测数据结果表明,PCWE能使信噪比约提高1.33 dB,标准差约减小18.33μV,表现出良好的去噪性能。本文研究结果表明,PCWE可以提高Spike信号的可靠性,或可为神经信号的编码解码提供一种新型有效的锋电位去噪方法。     

使用二维小波收缩法去除弹性成像蠕虫噪声

本文研究使用二维小波收缩去噪法去除弹性成像过程中产生的蠕虫噪声。先使用Sym8小波函数对含有蠕虫噪声的应变估计值矩阵进行3级二维离散小波分解,并使用Birg-éMassart算法获取二维小波变换的域值;然后分别使用硬域值函数和软域值函数对各尺度的水平方向、垂直方向、对角方向的高频系数进行量化;最后将第3层低频系数和各层被量化后的高频系数进行二维小波重构产生去噪后的弹性图像。仿真结果显示,提出的技术有效去除了弹性成像的蠕虫噪声,增强了弹性图像的信噪比(SNRe)和对比度噪声比(CNRe),提高了弹性图像与理想弹性图的相关系数(е);与二维低通滤波去噪法相比,使用二维小波收缩法产生的弹性图像有更高的SNRe和CNRe,能更清晰地显示硬物边界。同时,仿真结果也表明该技术对不同应变量的弹性图像的蠕虫噪声均能有效抑制。本研究表明二维小波收缩去噪法能有效去除弹性图像的蠕虫噪声并提高弹性图像性能。     

基于小波奇异点检测和阈值去噪的眨眼伪迹去除方法

目的眨眼伪迹是脑电中一种常见且影响严重的伪迹。本论文提出一种基于小波奇异点检测和阈值去噪的眨眼伪迹去除方法,无需眼电参考信号,做到自动去除单导脑电信号中的眨眼伪迹。方法首先利用小波奇异点检测特性以检测眨眼伪迹的峰值位置,然后只对眨眼伪迹区域进行小波阈值去噪。结果实验结果表明,本方法能够有效检测眨眼伪迹,避免了普通方法去噪时对非眨眼区域的影响。结论本方法使用的阈值和阈值函数优于典型的阈值和软、硬阈值函数,有效地去除了脑电中的眨眼伪迹。     

Homomorphic wavelet thresholding technique for denoising medical ultrasound images

A novel homomorphic wavelet thresholding technique for reducing speckle noise in medical ultrasound images is presented. First, we show that the speckle wavelet coefficients in the logarithmically transformed ultrasound images are best described by the Nakagami family of distributions. By exploiting this speckle model and the Laplacian signal prior, a closed form, data-driven, and spatially adaptive threshold is derived in the Bayesian framework. The spatial adaptivity allows the additional information of the image (such as identification of homogeneous or heterogeneous regions) to be incorporated into the algorithm. Further, the threshold has been extended to the redundant wavelet representation, which yields better results than the decimated wavelet transform. Experimental results demonstrate the improved performance of the proposed method over other well-known speckle reduction filters. The application of the proposed method to a realistic US test image shows that the new technique, named HomoGenThresh, outperforms the best wavelet-based denoising method reported in [1] by more than 1.6 dB, Lee filter by 3.6 dB, Kaun filter by 3.1 dB and band-adaptive soft thresholding [2] by 2.1 dB at an input signal-to-noise ratio (SNR) of 13.6 dB.     

ECG signal processing using multiresolution analysis

In this paper, multiresolution analysis using wavelets is discussed and evaluated in ECG signal processing. The approach we developed for processing the ECG signals uses two steps. In the first step, we implement an algorithm based on multiresolution analysis using discrete wavelet transform for denoising the ECG signals. The results we obtained on MIT-BIH ECG signals show good performance in denoising ECG signals. In the second step, multiresolution analysis is applied for QRS complex detection. It is shown that with such analysis, the QRS complex can be distinguished from high P or T waves, baseline drift and artefacts. The results we obtained on ECG signals from the MIT-BIH database show a detection rate of QRS complexes above 99.8% (sensitivity=99.88% and predictivity=99.89%), and a total detection failure of 0.24%.     

Automated wavelet denoising of photoacoustic signals for circulating melanoma cell detection and burn image reconstruction

Photoacoustic image reconstruction may involve hundreds of point measurements, each of which contributes unique information about the subsurface absorbing structures under study. For backprojection imaging, two or more point measurements of photoacoustic waves induced by irradiating a biological sample with laser light are used to produce an image of the acoustic source. Each of these measurements must undergo some signal processing, such as denoising or system deconvolution. In order to process the numerous signals, we have developed an automated wavelet algorithm for denoising signals. We appeal to the discrete wavelet transform for denoising photoacoustic signals generated in a dilute melanoma cell suspension and in thermally coagulated blood. We used 5, 9, 45 and 270 melanoma cells in the laser beam path as test concentrations. For the burn phantom, we used coagulated blood in 1.6 mm silicon tube submerged in Intralipid. Although these two targets were chosen as typical applications for photoacoustic detection and imaging, they are of independent interest. The denoising employs level-independent universal thresholding. In order to accommodate nonradix-2 signals, we considered a maximal overlap discrete wavelet transform (MODWT). For the lower melanoma cell concentrations, as the signal-to-noise ratio approached 1, denoising allowed better peak finding. For coagulated blood, the signals were denoised to yield a clean photoacoustic resulting in an improvement of 22% in the reconstructed image. The entire signal processing technique was automated so that minimal user intervention was needed to reconstruct the images. Such an algorithm may be used for image reconstruction and signal extraction for applications such as burn depth imaging, depth profiling of vascular lesions in skin and the detection of single cancer cells in blood samples.     

ECG signal processing using multiresolution analysis

In this paper, multiresolution analysis using wavelets is discussed and evaluated in ECG signal processing. The approach we developed for processing the ECG signals uses two steps. In the first step, we implement an algorithm based on multiresolution analysis using discrete wavelet transform for denoising the ECG signals. The results we obtained on MIT-BIH ECG signals show good performance in denoising ECG signals. In the second step, multiresolution analysis is applied for QRS complex detection. It is shown that with such analysis, the QRS complex can be distinguished from high P or T waves, baseline drift and artefacts. The results we obtained on ECG signals from the MIT-BIH database show a detection rate of QRS complexes above 99.8% (sensitivity = 99.88% and predictivity = 99.89%), and a total detection failure of 0.24%.     

基于小波包变换的肌电信号去噪研究

肌电信号的检测与分析对临床诊断以及康复医学具有重要意义.肌电信号的特点是强噪声背景下的生物信号,对肌电信号的检测和提取具有一定的难度.我们针对肌电信号的特点以及与噪声的关系,采用小波包变换的方法进行去噪研究.通过仿真以及在自主开发的便携肌电诱发电位测量系统中的应用,说明该方法对肌电信号的去噪是有效的.     

Electrocardiogram signals de-noising using lifting-based discrete wavelet transform

This paper introduces an effective technique for the denoising of electrocardiogram (ECG) signals corrupted by nonstationary noises. The technique is based on a second generation wavelet transform and level-dependent threshold estimator. Here, wavelet coefficients of ECG signals were obtained with lifting-based wavelet filters. A lifting scheme is used to construct second-generation wavelets and is an alternative and faster algorithm for a classical wavelet transform. The overall denoising performance of our proposed method is considered in relation to several measuring parameters, including types of wavelet filters (Haar, Daubechies 4 (DB4), Daubechies 6 (DB6), Filter(9-7), and Cubic B-splines), thresholding method, and decomposition depth. Three different kinds of noise were considered in this work: muscle artifact noise, electrode motion artifact noise, and white noise. Global performance is evaluated by means of the signal-to-noise ratio and visual inspection. Numerical results comparing the performance of the proposed method with that of nonlinear filtering techniques (median filter) are given. The results demonstrate consistently superior denoising performance of the proposed method over median filtering.     

脑电棘波识别和噪声消除的小波变换方法

研究了利用二进小波变的的模极大值识别脑电信号奇异点如棘波和消除噪声的方法，该方法在较好保留原脑电信号奇异信息的同时能有效地消除噪声，进一步讨论了信号与白噪声的奇异性指数的区别，以及小波变换模极大值沿各变换尺度传递的不同特性，并利用该特性区分信号中的奇异点和噪声，能准确识别奇异点的位置，这种奇异性识别技术在信号的特征提取和消除噪声方面有广阔的应用前景。     

基于自适应局部余弦变换的正交多普勒超声信号降噪方法的研究

多普勒超声信号的谱图已经被广泛用于医疗诊断。来自系统内部的噪声及外部的干扰会产生附加的频谱成分，从而影响谱图的主观分析及进一步的定量分析。为抑制噪声的影响，本文提出利用一种新的基于自适应局部余弦变换和非负Garrote取阈值的方法对正交多普勒超声信号进行降噪。首先，由正交信号提取正向和逆向血流信息；然后对其分别进行降噪；最后利用Hilbert变换进行重构得到真实信号的估计。在仿真研究中，采用平均频率波形和谱宽波形的估计精度作为性能改善的指标。结果表明这种方法优于基于小波变换的降噪方法，特别是在低信噪比情况下。