小波尺度谱阈值算法在膈肌肌电降噪的应用

为了去除膈肌肌电信号中的心电干扰,提出了一种小波尺度谱阈值的处理方法。首先,对信号进行五尺度的小波分解,并且把小波系数转化为小波尺度谱;其次,针对心电信号在尺度谱上高于周围两边信号的特点,提出尺度谱阈值对心电信号进行滤除;最后,小波尺度谱映射回小波系数,重构小波系数得到降噪后的膈肌肌电信号。对临床采集的膈肌肌电信号进行实验分析,并与小波阈值方法进行对比,结果表明本文方法降低了心电干扰并且保留膈肌肌电信号的特征。      

基于独立分量分析和小波变换的膈肌肌电信号降噪

提出了一种基于独立分量分析(ICA)和小波变换的处理方法,用于去除膈肌肌电信号中的心电干扰.首先利用独立成份分析法从膈肌肌电信号分解出心电独立成份,并对该心电成份选择合适的高通滤波器加以滤除,其它为膈肌肌电信号的独立分量进行五尺度小波分解以去除含心电的近似分量,再对各层细节分量进行小波重构,然后将处理后的全部独立分量反射投影回原始信号空间,最后,对临床采集的5路膈肌肌电信号进行实验分析,并与传统ICA方法进行对比.结果表明本文方法有更好的降低心电干扰性能.     

基于小波分析的心电信号去噪算法研究

心电信号在临床诊断上有非常重要的作用,但由于容易受到噪声干扰,采集的心电信号中通常包含很强噪声,为了有效去除噪声干扰,该文提出了一种基于自适应阈值的小波模极大值算法来进行信号去噪.关键点是在每个分解尺度上自适应的选取合适的阈值,用来对小波变换系数的模极大值点进行筛选,以去除噪声极值点.该文采用MIT/BIH数据库中的数据对算法进行仿真验证,结果表明该算法有更好的去噪效果,同时心电信号能被很好的保留.     

小波变换在心电信号去噪中的应用

文章根据coiflet小波在各个尺度上的不同的带通滤波特性,并利用小波变换多分辨的特点对心电信号进行滤波。文中通过软、硬阈值折衷函数及自适应阈值策略对MIT／BIH国际标准数据库中的ECG信号进行了处理与验证。实验结果表明,该算法能较好的抑制心电信号中的各类噪声干扰。     

基于小波变换的心电信号滤波算法

针对心电信号中含有的工频干扰、运动伪迹、肌电噪声和基线漂移四种噪声,提出一种以R波为优先准则,结合小波模极大值的逐拍滤波算法。该算法使用小波分解来消除心电信号中的基线漂移．采用小波模极大值法消除工频干扰和肌电噪声．利用小波分解各尺度间的相关性来消除运动伪迹。仿真实验结果表明,该算法平均信噪比达到22．3dB,说明其在有效改善信噪比的同时,能显著提高信号的分辨率。     

一种ECG信号肌电干扰去除方法的研究

实测心电(ECG)信号通常被多种因素干扰,尤其是肌电干扰的去除存在较大困难。本文提出一种结合经验模态分解法(EMD)与主成分分析(PCA)的消噪算法来去除ECG信号的肌电干扰。解决了通常采用小波算法和EMD等方法会导致ECG信号产生振荡和丢失有用信息的难题。本研究利用PCA对含噪信号经EMD分解后的内蕴模态函数(IMF)进行去噪处理,通过对MIT-BIH心电数据进行仿真,以及定性分析了信噪比(SNR)和均方误差(MSE)。结果表明,ECG信号中的肌电干扰被有效去除,所提方法的消噪效果整体上优于小波去噪算法和EMD消噪算法,是一种有效的消噪方法。     

基于改进协同量子粒子群的小波去噪分析研究

小波阈值函数中,因信号之间的不连续性及小波估计系数与原信号的小波系数存在误差等原因,图像无法得到最优还原.为此提出一种基于改进协同量子粒子群算法优化小波函数的去噪方法.该方法在协同量子粒子群优化(CQPSO)算法的基础上引入了自适应收缩扩张因子,用改进的协同量子粒子群算法优化小波阈值函数中的调节因子和阈值.仿真图像和数...     

基于小波阈值自适应的局部放电信号降噪

为了抑制电缆绝缘局部放电检测中存在的白噪声干扰,提出一种基于小波阈值的局部放电特征提取的方法.对含噪信号进行小波分解,在选取最优阈值时,用鲸鱼算法优化阈值选取过程,有效提高了算法的精度与运算速度.为验证去噪效果,利用该方法和传统小波软阈值法对局部放电仿真信号和实测局部放电信号去噪,结果表明:与传统软阈值函数法相比,该方...     

小波包多阈值法在地震信号去噪中的应用研究

地震信号中通常含有各种干扰噪声,严重影响了地震资料的信噪比和分辨率,小波包变换是地震资料去噪的有效方法之一。针对传统小波包阈值去噪不明显和存在失真的问题,提出一种基于多阈值函数的小波包地震信号去噪方法。对地震波信号进行小波包分解,并对小波包分解系数按照频率大小的顺序进行排列,根据分解的系数处于不同频带选取不同的阈值准则进行去噪处理,对得到的系数进行重构,可有效地去除地震信号中的噪声。对仿真地震信号以及实际地震信号进行小波包多阈值去噪处理,实验结果表明,该方法较好地去除了干扰噪声保留了有用信号,去噪效果明显且失真小,有效地提高了地震资料的分辨率。     

基于形态学运算和自适应阈值的心电信号消噪

抑制信号中的噪声干扰,是心电(ECG)信号预处理中的关键步骤.针对传统形态学滤波损失有用信号的缺陷,本文提出了一种基于形态学运算和自适应阈值的ECG信号消噪算法.首先,对含噪ECG信号进行形态学滤波和形态学峰谷提取运算;然后,估算形态学峰谷信号中时变噪声的即时方差,并依据3σ准则对峰谷信号进行自适应阈值处理,保留其中的有用信号;最后,将阈值处理结果与形态学滤波结果相加,作为ECG信号消噪处理的最终结果.仿真试验与实际应用结果表明,该算法不仅可以有效去除时变噪声的干扰,而且较好地保持了ECG信号的特征形态,处理效果明显优于以往的形态学滤波算法,且比基于平稳小波变换的消噪算法更适用于非平稳ECG信号的消噪处理.     

An efficient coding algorithm for the compression of ECG signals using the wavelet transform

A wavelet-based electrocardiogram (ECG) data compression algorithm is proposed in this paper. The ECG signal is first preprocessed, the discrete wavelet transform (DWT) is then applied to the preprocessed signal. Preprocessing guarantees that the magnitudes of the wavelet coefficients be less than one, and reduces the reconstruction errors near both ends of the compressed signal. The DWT coefficients are divided into three groups, each group is thresholded using a threshold based on a desired energy packing efficiency. A binary significance map is then generated by scanning the wavelet decomposition coefficients and outputting a binary one if the scanned coefficient is significant, and a binary zero if it is insignificant. Compression is achieved by 1) using a variable length code based on run length encoding to compress the significance map and 2) using direct binary representation for representing the significant coefficients. The ability of the coding algorithm to compress ECG signals is investigated, the results were obtained by compressing and decompressing the test signals. The proposed algorithm is compared with direct-based and wavelet-based compression algorithms and showed superior performance. A compression ratio of 24:1 was achieved for MIT-BIH record 117 with a percent root mean square difference as low as 1.08%.     

A 2-D ECG compression method based on wavelet transform and modified SPIHT

A two-dimensional (2-D) wavelet-based electrocardiogram (ECG) data compression method is presented which employs a modified set partitioning in hierarchical trees (SPIHT) algorithm. This modified SPIHT algorithm utilizes further the redundancy among medium- and high-frequency subbands of the wavelet coefficients and the proposed 2-D approach utilizes the fact that ECG signals generally show redundancy between adjacent beats and between adjacent samples. An ECG signal is cut and aligned to form a 2-D data array, and then 2-D wavelet transform and the modified SPIHT can be applied. Records selected from the MIT-BIH arrhythmia database are tested. The experimental results show that the proposed method achieves high compression ratio with relatively low distortion and is effective for various kinds of ECG morphologies.     

Compression of biomedical signals with mother wavelet optimization and best-basis wavelet packet selection

We propose a novel scheme for signal compression based on the discrete wavelet packet transform (DWPT) decompositon. The mother wavelet and the basis of wavelet packets were optimized and the wavelet coefficients were encoded with a modified version of the embedded zerotree algorithm. This signal dependant compression scheme was designed by a two-step process. The first (internal optimization) was the best basis selection that was performed for a given mother wavelet. For this purpose, three additive cost functions were applied and compared. The second (external optimization) was the selection of the mother wavelet based on the minimal distortion of the decoded signal given a fixed compression ratio. The mother wavelet was parameterized in the multiresolution analysis framework by the scaling filter, which is sufficient to define the entire decomposition in the orthogonal case. The method was tested on two sets of ten electromyographic (EMG) and ten electrocardiographic (ECG) signals that were compressed with compression ratios in the range of 50%-90%. For 90% compression ratio of EMG (ECG) signals, the percent residual difference after compression decreased from (mean +/- SD) 48.6 +/- 9.9% (21.5 +/- 8.4%) with discrete wavelet transform (DWT) using the wavelet leading to poorest performance to 28.4 +/- 3.0% (6.7 +/- 1.9%) with DWPT, with optimal basis selection and wavelet optimization. In conclusion, best basis selection and optimization of the mother wavelet through parameterization led to substantial improvement of performance in signal compression with respect to DWT and randon selection of the mother wavelet. The method provides an adaptive approach for optimal signal representation for compression and can thus be applied to any type of biomedical signal.     

Genetic algorithm and wavelet hybrid scheme for ECG signal denoising

This paper introduces an effective hybrid scheme for the denoising of electrocardiogram (ECG) signals corrupted by non-stationary noises using genetic algorithm (GA) and wavelet transform (WT). We first applied a wavelet denoising in noise reduction of multi-channel high resolution ECG signals. In particular, the influence of the selection of wavelet function and the choice of decomposition level on efficiency of denoising process was considered. Selection of a suitable wavelet denoising parameters is critical for the success of ECG signal filtration in wavelet domain. Therefore, in our noise elimination method the genetic algorithm has been used to select the optimal wavelet denoising parameters which lead to maximize the filtration performance. The efficiency performance of our scheme is evaluated using percentage root mean square difference (PRD) and signal to noise ratio (SNR). The experimental results show that the introduced hybrid scheme using GA has obtain better performance than the other reported wavelet thresholding algorithms as well as the quality of the denoising ECG signal is more suitable for the clinical diagnosis.     

Enhanced real-time ECG coder for packetized telecardiology applications.

A new real-time compression method for electrocardiogram (ECG) signals has been developed based on the wavelet transform approach. The method is specifically adaptable for packetized telecardiology applications. The signal is segmented into beats and a beat template is subtracted from them, producing a residual signal. Beat templates and residual signals are coded with a wavelet expansion. Compression is achieved by selecting a subset of wavelet coefficients. The number of selected coefficients depends on a threshold which has different definitions depending on the operational mode of the coder. Compression performance has been tested using a subset of ECG records from MIT-BIH Arrhythmia database. This method has been designed for real-time packetized telecardiology scenarios both in wired and wireless environments.     

雷达辐射源信号聚类分选算法

基于脉内特征参数的聚类是实现雷达辐射源信号分选的一种重要途径.本文在小波域滤波算法的基础上提出一种新的雷达辐射源信号脉内特征提取和聚类分选方法,将小波变换后的低频逼近小波系数的能量分布熵与经过尺度相关去噪计算后反映信号边缘的高频细节小波系数能量分布熵作为分选的脉内特征向量,并引入灰关联测度来衡量脉内特征样本之间的相似程...     

建立在小波变换基础上的心率变异信号的仿真建模和分解

心率变异性（HeartRateVariability，简记为HRV）是无创检测心脏自主神经调节功能的一种手段，是近年来心电信号处理领域的一个前沿研究热点．考虑到HRV仿真建模的重要性和对HRV信号的1／f成分和非1／f成分分解开，分别处理的必要性，本文建立了基于小波逆变换的HRV信号仿真模型。用该模型仿真的HRV信号不仅包含谐波部分还反映了1／f部分，并且能准确地控制谐波部分的频率。仿真结果表明该模型能较好地仿真出时域、频域特征都接近实际HRV信号的HRV信号．针对1／f过程的特点，本文提出了基于小波变换的方法将HRV信号的1／f成分和非1／f成分在时域上分解开的算法．分解结果表明服这种算法的可行性。     

A COMPRESSION ALGORITHM FOR ECG BASED ON INTEGER LIFTING SCHEME WAVELET TRANSFORM

In view of the shortcomes of conventional ElectroCardioGram (ECG) compression algo- rithms,such as high complexity of operation and distortion of reconstructed signal,a new ECG compression encoding algorithm based on Set Partitioning In Hierarchical Trees (SPIHT) is brought out after studying the integer lifting scheme wavelet transform in detail.The proposed algorithm modifies zero-tree structure of SPIHT,establishes single dimensional wavelet coefficient tree of ECG signals and enhances the efficiency of SPIHT-encoding by distributing bits rationally,improving zero-tree set and ameliorating classifying method.For this improved algorithm,floating-point com- putation and storage are left out of consideration and it is easy to be implemented by hardware and software.Experimental results prove that the new algorithm has admirable features of low complexity, high speed and good performance in signal reconstruction.High compression ratio is obtained with high signal fidelity as well.     

Multiple scale correlation of signals by shift-invariant discrete wavelet transform

Correlation of signals at multiple scales of observation is useful for multiresolution interpretation of image, data and target signature analysis. Multiresolution analysis is inherent in the discrete wavelet transform (DWT), but shift-variance of the coefficients of the transform in dyadic orthogonal and biorthogonal basis spaces is the problem associated with it. Shift-variance of the transform and absence of a direct transform domain relationship make correlation of signals by the DWT inconvenient at multiple scales. The circulant shift property of the DWT coefficients is used in a novel way to produce correlation of signals at multiple scales with the critically sampled DWT only. The algorithm is derived in both discrete time and z-domain for signal vectors of finite duration. The algorithm is independent of signal waveform and wavelet kernel and is applied particularly for multiple scale correlation of radar signals, namely linear frequency modulated (LFM) chirp signals.