心电信号的小波变换滤波算法的改进

对心电信号的滤波算法进行了改进。在利用小波变换实现心电图信号滤波算法的基础上，增加了对2^3尺度下小波分解所得细节信号的模极大值对的检测功能，以修复因滤波受损的心电信号的QRS波。经MIT／BIH标准心电数据库验证，试验表明，该方法行之有效。     

基于提升小波变换和多种策略的QRS波检测算法

目的：为了提高计算机处理心电信号的速率和精度,提出了一种基于提升小波变换,结合多种策略的QRS波检测算法。方法：首先采用基于阀值的提升小波去噪方法去除心电信号中的高频白噪声和低频基线漂移;再对处理后的心电信号进行提升小波分解,得出各层逼近信号和细节信号,在第3尺度上采用模极大值阀值法对R波进行检测．找出备选的R波,同时采用几何的方法定位Q波和S波及QRS波起点和终点;最后采用补偿法、波宽法及QRS波时长法对QRS波群进行纠正。结果：本文算法在时域心电图上实现了QRS波的准确定位．提取了心电图的QRS波段。通过MIT—BIH数据库验证,本算法具有很好的表现。结论：实验结果表明,相比传统的算法,本文采用的提升小波和多种策略的检测算法．能有效的检测QRS波,为心电信号的自动识别奠定了基础。     

心电信号的小波变换滤波算法的改进

对心电信号的滤波算法进行了改进。在利用小波变换实现心电图信号滤波算法的基础上，增加了对2^3尺度下小波分解所得细节信号的模极大值对的检测功能，以修复因滤波受损的心电信号的QRS波。经MIT/BIH标准心电数据库验证，试验表明，该方法行之有效。     

正交小波变换的快速算法在心电QRS波检测中的应用

目的：研究基于小波变换的心电QRS波检测的准确率、抗干扰性和实时性，论证其在实际工程应用中的可行性。方法：作者在比较了不同小波基的检测准确率之后，采用一种基于三次B样条小波变换的心电QRS波检测算法，利用离散正交二进小波的快速算法-Mallat算法进行分解滤波，再利用小波变换与信号奇异点的关系，在2^3尺度下识别R波峰值，在2^1尺度上检测QRS波的起点和终点，QRS波的起点和终点对应于小波变换的一对符号相反的模极大值，R波的峰点对应于介于这对模极大值之间的小波变换过零点，并用美国MIT／BIH心电标准数据库分析该算法的准确率、抗干扰性和实时性。结果：该方法具有比较理想的检测准确率，在99％以上；对肌电、工频、基漂等常见的心电信号干扰有较好的容限度，即使心电序列伴有严重的基漂和高频、工频、肌电等干扰，也不影响QRS波的检测；此外，三次B样条小波基的滤波器个数少，提高了运算速度，采样11．4s的数据进行分析，耗时为0．2s～0.3S，实时效果较明显。结论：可以满足实际工程应用的需要。     

基于变分模态分解的心电信号QRS波检测

为了解决含噪心电信号QRS波的提取问题,本研究提出了一种基于变分模态分解(variation mode decomposition,VMD)的心电信号QRS波群检测和定位的方法。首先确定合适的分解层数,利用变分模态分解将心电信号分解为一系列模态分量。对每层模态分量进行分析,选取含有QRS波的模态分量层。通过小波变换的奇异值检测原理,确定心电信号的奇异值,定位心电信号R波的峰值位置,再检测QRS波的波形宽度。实验证明了该方法对含噪的QRS波检测准确度在96%以上,能够准确的检测和定位心电信号QRS波。     

基于小波变换与形态学运算的ECG综合检测算法的研究

针对心电波形检测中小波变换算法的缺点 ,在 ECG特征点检测中 ,将原始信号在 3尺度上的 haar小波分解的细节信号模极大值对检测法与数学形态学峰谷检测相结合 ,提出了一种新的心电波形特征点综合检测算法 ,该算法弥补了小波变换算法对信号振幅检测上的不足 ,有效地提高了心电信号特征点检测的准确度。     

ECG信号的小波变换检测方法

本文反小波变换应用于ＥＣＧ信号的ＱＲＳ波检测。利用二进样条小波对信号按Ｍａｌｌａｔ算法进行变换：从二进小波变换的等效滤波器的角度，分析了信号奇异点（Ｒ峰点）与其小波变换模极大值对的零交叉点的关系。在检测中运用了一系列策略以增强算法的抗干扰能力、提高ＱＲＳ波的正确检测率。经ＭＩＴ／ＢＩＨ标准心电数据库检测验证，ＱＲＳ波正确检测率高达９９．８％。     

基于小波变换的QRS波群实时检测算法

本文研究了基于小波变换方法的心电信号QRS波群检测算法,通过对心电信号进行低通滤波、小波变换、差分平滑、阈值检测和修正策略等技术,提高了QRS波群的检测率.经MIT-BIH心律失常心电数据库全部48例数据的检验,QRS波检测灵敏度达99.82%,真阳性率达99.52%.在Windows环境下可实时实现.     

基于DSP的R波检测快速算法研究

提出了一种实用的基于DSP的R波快速检测算法。根据心电图中R波的特点和长度变换的原理，对心电信号进行差分加滑动窗积分的运算，然后根据信号特点动态调整检测阈值和盲区，并在TI公司的DSP上实现了R波的实时检测，保证了较好的检测率。通过对MIT—BIH典型心电数据检测，可以满足心电分析的要求。     

基于小波变换的心电图QRS波群检测方法研究

本文就心电图信号的QRS波群检测提出了一种基于小波变换的信号特征提取方法,此方法对心电信号中QRS波群的时变特性及几种常见的心电干扰具有较强的鲁棒性.文中我们采用两种不同性质的小波为母小波对含有噪声污染的心电信号进行多尺度的小波分解,在没有预先消噪处理的情况下,较为准确、快速地检测出QRS波群的信息,并且以国际上广泛承认的心电数据库MIT-BIH中的记录对算法进行检验.     

Separation of electrocardiographic and encephalographic components based on signal averaging and wavelet shrinkage techniques

During ambulatory monitoring, it is often required to record the electroencephalogram (EEG) and the electrocardiogram (ECG) simultaneously. It would be ideal if both EEG and ECG can be obtained with one measurement. We introduce an algorithm combining the wavelet shrinkage and signal averaging techniques to extract the EEG and ECG components from an EEG lead signal to a noncephalic reference (NCR). The evaluation using simulation data and measured data showed that the normalized power spectrum unvaried in all frequency bands for the EEG components, and the sensitivity and specificity of R-wave detection for the ECG component were nearly 100%.     

小波变换去除心电信号中呼吸信号干扰

目的 研究用小波变换去除心电图信号中呼吸信号的方法。方法 采用db4小波对采样频率为200Hz的心电图信号作离散小波变换的多层分解，并与呼吸信号的频率成分比较，发现呼吸信号分布在心电图信号分解后第8、9、10层细节中，去除这些成分和高频干扰，对剩下的分量重构。结果 比较成功地纠正了心电信号的基线，去除了低频呼吸信号的干扰。结论 小波变换的方法能够去除心电信号中的呼吸信号干扰。     

An improved algorithm for respiration signal extraction from electrocardiogram measured by conductive textile electrodes using instantaneous frequency estimation

In this paper, an improved algorithm for the extraction of respiration signal from the electrocardiogram (ECG) in home healthcare is proposed. The whole system consists of two-lead electrocardiogram acquisition using conductive textile electrodes located in bed, baseline fluctuation elimination, R-wave detection, adjustment of sudden change in R-wave area using moving average, and optimal lead selection. In order to solve the problems of previous algorithms for the ECG-derived respiration (EDR) signal acquisition, we are proposing a method for the optimal lead selection. An optimal EDR signal among the three EDR signals derived from each lead (and arctangent of their ratio) is selected by estimating the instantaneous frequency using the Hilbert transform, and then choosing the signal with minimum variation of the instantaneous frequency. The proposed algorithm was tested on 15 male subjects, and we obtained satisfactory respiration signals that showed high correlation (r ² > 0.8) with the signal acquired from the chest-belt respiration sensor.     

基于小波变换和似然无偏估计的运动心电信号伪差消除法

介绍了一种基于小波变换并结合似然无偏估计来消除运动心电信号中基线漂移和肌电噪声的新方法 ,且提出了评价心电消噪算法有效性的两个指标。该方法利用小波变换多分辨率分析的特性 ,将原始运动心电信号进行多尺度分解及单支重构 ,根据运动心电信号的自身特征 ,结合似然无偏估计针对不同的心电细节成分进行阈值消噪处理。研究结果表明 ,该方法能有效消除运动心电信号中的干扰成分 ,为进一步研究运动心电信号的特征识别分析提供了新途径。     

基于连续小波变换的心电信号特征提取及其仪器实现

介绍了一种用于心电信号的记录和识别的虚拟式测量和分析仪器系统，目的是要构建一种基于PC的虚拟仪器．能够实现十二导联心电信号的同步记录、同步整体观察及测量12导联同一心动周期的波形，从而提高心电参数测量的准确性。同时，由于Mexican hat小波特有的时域特性，对QRS波群具有很好的定位特性和分析精度，因此在本仪器中利用连续小波变换，选用Mexicanhat作为小波基，对心电信号中的特征信息进行精确检测，并给出准确的心电信号特征描述参数。对临床实测心电信号的分析表明，即使在有严重噪声干扰的情况下，本方法也很容易实现对心电信号特征信息的精确描述，并且具有很高的实时性，从而在本仪器中获得了实际和有效的应用。     

基于功率谱特征的心电数据小波压缩方法

利用心电功率谱特征,探索心电数据压缩新方法。用小波分解心电信号为高频与低频分量,对低频分量继续分解达到要求的级数,对高频分量则根据其所在频段的能量,对临床诊断的价值加以取舍。对MIT生理信号数据库心电数据的压缩与还原分析表明,该方法平衡了压缩比与还原精度之间的矛盾,既具有较高的压缩比,又具有较高的还原精度,而且对信号的适应性也明显增强。另外,该压缩方法还具有一定的去噪作用。说明结合心电功率谱特征与小波变换方法压缩心电有其优势。     

ECG feature extraction and disease diagnosis

An important factor to consider when using findings on electrocardiograms for clinical decision making is that the waveforms are influenced by normal physiological and technical factors as well as by pathophysiological factors. In this paper, we propose a method for the feature extraction and heart disease diagnosis using wavelet transform (WT) technique and LabVIEW (Laboratory Virtual Instrument Engineering workbench). LabVIEW signal processing tools are used to denoise the signal before applying the developed algorithm for feature extraction. First, we have developed an algorithm for R-peak detection using Haar wavelet. After 4th level decomposition of the ECG signal, the detailed coefficient is squared and the standard deviation of the squared detailed coefficient is used as the threshold for detection of R-peaks. Second, we have used daubechies (db6) wavelet for the low resolution signals. After cross checking the R-peak location in 4th level, low resolution signal of daubechies wavelet P waves and T waves are detected. Other features of diagnostic importance, mainly heart rate, R-wave width, Q-wave width, T-wave amplitude and duration, ST segment and frontal plane axis are also extracted and scoring pattern is applied for the purpose of heart disease diagnosis. In this study, detection of tachycardia, bradycardia, left ventricular hypertrophy, right ventricular hypertrophy and myocardial infarction have been considered. In this work, CSE ECG data base which contains 5000 samples recorded at a sampling frequency of 500 Hz and the ECG data base created by the S.G.G.S. Institute of Engineering and Technology, Nanded (Maharashtra) have been used.     

ECG analysis using wavelet transform: application to myocardial ischemia detection

In this paper, we propose a method for the detection of myocardial ischemic events from electrocardiogram (ECG) signal using the wavelet transform technique. The wavelet transform is obtained using the quadratic spline wavelet. Then, based on the wavelet transform values, the characteristic points of the ECG signal are found out. These characteristic points are used to identify any ischemic episodes present in the ECG signal. This technique can be extended for other types of cardiac abnormality detections, which induce changes in the ECG.     

An ECG signal compressor based on the selection of optimal threshold levels of discrete wavelet transform coefficients

Compression of electrocardiography (ECG) is necessary for efficient storage and transmission of the digitized ECG signals. Discrete wavelet transform (DWT) has recently emerged as a powerful technique for ECG signal compression due to its multi-resolution signal decomposition and locality properties. This paper presents an ECG compressor based on the selection of optimum threshold levels of DWT coefficients in different subbands that achieve maximum data volume reduction while preserving the significant signal morphology features upon reconstruction. First, the ECG is wavelet transformed into m subbands and the wavelet coefficients of each subband are thresholded using an optimal threshold level. Thresholding removes excessively small features and replaces them with zeroes. The threshold levels are defined for each signal so that the bit rate is minimized for a target distortion or, alternatively, the distortion is minimized for a target compression ratio. After thresholding, the resulting significant wavelet coefficients are coded using multi embedded zero tree (MEZW) coding technique. In order to assess the performance of the proposed compressor, records from the MIT-BIH Arrhythmia Database were compressed at different distortion levels, measured by the percentage rms difference (PRD), and compression ratios (CR). The method achieves good CR values with excellent reconstruction quality that compares favourably with various classical and state-of-the-art ECG compressors. Finally, it should be noted that the proposed method is flexible in controlling the quality of the reconstructed signals and the volume of the compressed signals by establishing a target PRD and a target CR a priori, respectively.     

基于DSP实现ECG信号的小波变换

本文比较了各种实现小波变换方法的优缺点,采用TMS320F206系列的DSP系统实现ECG信号的小波变换,利用多孔算法实现一尺度到四尺度的Marr小波变换.本系统对6K的ECG信号处理需要400ms的时间.采用硬件DSP的方法大大提高了小波变换的速度,其结果可以用于R波或异常心电检测的实际应用.