小波变换在生物医学信号处理中的应用

小波变换在生物医学信号处理中的应用１生物医学应用领域的小波性质小波可分解为以下二部分：重复信息（可进行连续小波变换［ＣＷＴ］或小波帧变换）和非重复信息（正交、半正交或双正交基波信号）。重复信号通常作为信号分析特征提取和处理的首选信息，因为其提供了真实...     

时频分析方法及其在医学信号处理中的应用

许多生物医学信号的频率分布随着时间迅速变化，传统的傅立叶谱分析技术显然不能满足对这类时变信号的分析。通过对一个一维时间或频率信号进行变换，其时频表示能够在时间--频率平面上描述信号的能量分布。实际研究表明，时频分析方法能够解决很多生物医学信号问题。着重论述时频分析的基本方法及其在生物医学信号处理中的应用。     

小波分析在医学中的应用

小波分析方法是一种新的信号分析方法 ,由于其具有多分辨分析的特点 ,能较好地突出信号局部特征 ,在微弱、背景噪声较强的随机信号分析中具有重要的意义。本文综述了这一新方法在诸如 EEG、EP、ECG、医学图像等生物医学信号中的广泛应用 ,并对各自效果进行了分析。最后 ,对小波变换在生物医学信号处理中的发展作了展望。     

小波分析在医学中的应用

小波分析方法是一种新的信号分析方法，由于其具有多分辨分析的特点，能较好地突出信号局部特征，在微弱，背景噪声较强的随机信号分析中具有重要的意义。本综述了这一新方法在诸如EEG，EP，ECG，医学图像等生物医学信号中的广泛应用，并对各自效果进行了分析，最后，对小波变换在生物医学信号处理中的发展作了展望。     

小波技术在生物医学成像中的应用

近年来，小波技术广泛地用于信号处理中，取得了令人瞩目的效果，在生物医学信号处理领域和医学成像中也被作为一个有利的工具。本文简要综述了小波技术在生物医学成像中的应用。     

小波技术在生物医学成像中的应用

近年来，小波技术广泛地用于信号处理中，取得了令人瞩目的效果，在生物医学信号处理领域和医学成像中也被作为一个有利的工具。本文简要综述了小波技术在生物医学成像中的应用。     

心音的分析

自上世纪70年代以来,由于计算机和现代数字信号处理技术的发展,生物医学工作者对心音进行了大量的分析研究,相继运用了谱分析、时频分析、小波变换和最优匹配法等方法.谱分析不适合对非平稳随机心音信号的分析.国内外工作者通过加核函数和采用模糊函数等方法来减少时频分布中的交叉项.具有自适应性时-频窗的小波变换可以得到更能反映心音病例特征的信息.最优匹配法是一种没有交叉项的时频分析方法.     

生物医学信号的小波分析方法

本文比较了傅立叶变换、加窗傅立叶变换和小波变换等处理信号的方法优缺点 ,并在此基础上着重介绍了小波分析的基本概念及其在心电信号处理中的应用和实现方法     

生物医学信号的小波分析方法

本比较了傅立叶变换、加窗傅立叶变换和小波变换等处理信号的方法优缺点,并在此基础上着重介绍了小波分析的基本概念及其在心电信号处理中的应用和实现方法。     

基于多分辨分析与连续小波变换提取和分析兔体感诱发电位

研究单次提取兔体感诱发电位,并定位和分析诱发电位波形成分。麻醉兔,以0.5Hz频率电脉冲刺激兔下肢隐神经,3764Hz采样率收集兔头皮电位。采用一维多分辨分析提取兔体感诱发电位,并用连续小波变换定位和分析诱发电位波形成分。单次诱发电位的小波变换与叠加平均诱发电位比较,表明Daubechies小波多分辨分析可以单次提取诱发电位。连续小波变换能够精确定位诱发电位中波形成分,并可采用连续小波变换分析诱发成分的频域特性。连续小波变换技术把一维时域信号投影到二维时频空间研究将成为医学信号处理的一个有用方法。     

利用小波变换去除针电极肌电信号噪声的实验研究

小波变换能将各种交织在一起的由不同频率组成的混合信号分解成不相同频段的信号。本文采用小波变换的方法对针电极肌电信号（ＮＥＭＧ）进行去噪声处理,并进行对比实验。结果表明,在应用小波变换处理ＮＥＭＧ信号过程中,只要适当选取变换尺度就可以在处理信号的同时,有效地消除原信号中高频噪声和基线漂移的影响。     

小波变换在心电信号滤波处理中的应用研究

介绍用小波实现心电图信号滤波处理的方法。该方法采用小波变换将原始心电信号分解为不同频段下的细节信号，去掉某些细节信号，再用小波逆变换恢复保留信号，就能实现心电信号的三种主要的消除。     

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。     

QRS detection using new wavelets

This paper deals with a new wavelet (WT) which has been developed and very effectively and efficiently used for the detection of QRS segments from the ECG signal. After carrying out the detection using five existing wavelets (two symmetric-- WT1 and WT2--and three asymmetric--WT3, WT4 and WT5), two new wavelets (WT6 and WT7) were constructed and used for QRS detection. WT6 is a symmetric wavelet and has been constructed by a trial-and-error method. WT7 is an adaptive symmetric wavelet and adjusts its threshold as per the amplitude of the ECG signal. The accuracy of QRS detection obtained from WT6 is 99.8% and from WT7 100%. The CSE DS-3 database has been used for tests. Both WT6 and WT7 have been proved to be superior in performance to the existing wavelets. Out of WT6 and WT7, WT7 holds high promise for error-free reliable QRS detection in computer-aided feature extraction and disease diagnostics.     

QRS detection using new wavelets

This paper deals with a new wavelet (WVT) which has been developed and very effectively and efficiently used for the detection of QRS segments from the ECG signal. After carrying out the detection using five existing wavelets (two symmetric--WT1 and WT2--and three asymmetric--WT3, WT4 and WT5), two new wavelets (WT6 and WT7) were constructed and used for QRS detection. WT6 is a symmetric wavelet and has been constructed by a trial-and-error method. WT7 is an adaptive symmetric wavelet and adjusts its threshold as per the amplitude of the ECG signal. The accuracy of QRS detection obtained from WT6 is 99.8 % and from WT7 100%. The CSE DS-3 database has been used for tests. Both WT6 and WT7 have been proved to be superior in performance to the existing wavelets. Out of WT6 and WT7, WT7 holds high promise for error-free reliable QRS detection in computer-aided feature extraction and disease diagnostics.     

Pattern identification of biomedical images with time series: Contrasting THz pulse imaging with DCE-MRIs

ObjectiveWe provide a survey of recent advances in biomedical image analysis and classification from emergent imaging modalities such as terahertz (THz) pulse imaging (TPI) and dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) and identification of their underlining commonalities.MethodsBoth time and frequency domain signal pre-processing techniques are considered: noise removal, spectral analysis, principal component analysis (PCA) and wavelet transforms. Feature extraction and classification methods based on feature vectors using the above processing techniques are reviewed. A tensorial signal processing de-noising framework suitable for spatiotemporal association between features in MRI is also discussed.ValidationExamples where the proposed methodologies have been successful in classifying TPIs and DCE-MRIs are discussed.ResultsIdentifying commonalities in the structure of such heterogeneous datasets potentially leads to a unified multi-channel signal processing framework for biomedical image analysis.ConclusionThe proposed complex valued classification methodology enables fusion of entire datasets from a sequence of spatial images taken at different time stamps; this is of interest from the viewpoint of inferring disease proliferation. The approach is also of interest for other emergent multi-channel biomedical imaging modalities and of relevance across the biomedical signal processing community.     

A Study on sEMG Simulation Modeling and Its Decomposition Methods

In this paper, we establish a surface electromyography(sEMG) signal model and study the signal decomposition method from noisy background. Firstly, single fiber action potential (SFAP), motor unit action potential (MUAP) and motor unit action potential train(MUAPT) are simulated based on the tripolar signal source model, and then the sEMG is obtained; secondly, the simulated sEMG signal is extracted from the mixed signals that consists of white noises, power frequency interference signal and electrocardio signal by independent component analysis (ICA) algorithms; lastly, the spikes corresponding to each motor unit action potential from the simulated sEMG signals were detected by applying the wavelet transform (WT) method. Simulation results showed that sEMG model could describe the physiological process of sEMG, ICA and WT methods could extract the sEMG signal and its features, which will lay a foundation for further classifying the MUAP.     

整体生理信号表现型到基因型--跨入生物医学信号处理的新时代

过去，生理学家忙于记录和分析各种生理信号；从工程领域过来的生物医学信号处理专家，则忙于把工程上研究的各种信号处理方法移植到生物医学信号处理领域中来；分子生物学家则多忙于研究基因及其表达与调控。如何将整体生理信号表现型与基因型结合起来研究，特别是用能无创获得的、重复性好的整体电生理信号所含的信息（表现型）去预测基因型方面的工作，虽然报导很少，但可以预断，可能是今后非常重要的研究领域。