基于Mexican-hat小波的QRS检测新方法

基于心电信号的特征点对应于Mexican—hat小波变换的极值，我们使用Mexican—hat小波检测心电信号的特征点，为心电信号分析提供了新的检测手段。该方法简单，对心电信号特征点定位准确，快速。经MIT—BIH心电数据库检验，QRS波的检测率达到99．9％。     

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％。     

基于小波变换的心电信号去噪处理

人体心电信号随着检测状态及时间的变化具有明显的非平稳性及包含许多干扰的特点.本文将小波变换的时频定位特性运用于心电信号的测量,利用小波变换多尺度多分辨的特点对心电信号进行分解,不同频带的信号便显现在小波分解的不同尺度上.进行信号重构时,去除各种干扰成份,从而获得精确的心电波形,为医疗诊断提供了更加准确的依据.     

基于抗混叠小波变换的胎儿心电信号分离方法

目的胎儿心电图能够较好地反映胎儿在子宫内的发育状况,但是由于采集的胎儿心电信号中混有噪声干扰,给医学诊断带来极大干扰。抗混叠小波变换算法能够从混有噪声干扰的源信号中提取胎儿心电信号,且当胎儿心电信号与母体心电信号混叠时,该方法仍能够提取胎儿心电信号。基于此,本文提出一种基于抗混叠小波变换的胎儿心电信号分离方法。方法首先对原始心电信号进行滤波预处理,再利用小波变换分离母体心电信号和胎儿心电信号,最后根据抗混叠分离算法获取混合心电信号中的胎儿心电信号,得到满周期的胎儿心电信号。结果该方法能够较好地获取胎儿心电波形,胎儿心电波形识别准确率可达100%,在信噪比较低的情况下,识别准确率仍可达到77.78%。应用此算法在国外MIT-BIT心电信号数据和国内医院临床心电信号数据中进行实验仿真,并与先前学者的胎儿心电信号提取方法进行对比。结论此方法具有较高的识别准确率以及在临床应用中的可靠性和可行性。     

ECG信号小波变换与峰谷检测算法的研究

本文在ECG信号检测过程中，将ECG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与数学形态学峰谷检测相结合，提出了ECG信号小波变换与峰谷检测算法，该算法弥补了小波变换算法对ECG信号时域特征检测的不足，有效地提高了ECG信号检测的准确度。     

主成分分析和线性判别分析应用于心电信号特征提取和诊断算法研究

针对心脏疾病发病率高且不易自主检测的问题,提出了一种心电信号特征提取和分类诊断算法。首先对心电信号进行提升小波变换和改进半软阈值相结合的预处理变换,在去除心电信号的噪声后,利用主成分分析(principal component analysis,PCA)对心电信号进行降维,并利用核独立成分提取心电信号的非线性特征;同时离散小波变换提取去噪后心电信号的频域特征,基于线性判别分析(linear discriminant analysis, LDA)对频域统计特征进行降维处理。将两种不同的特征向量组成多域特征空间,最后利用支持向量机对多域特征空间分类,遗传算法对其参数进行寻优,从而实现心电信号特征的分类。实验结果表明,所提出的算法能够对5类心电节拍进行准确分类,分类效率达99.11%。     

基于小波变换和矢量量化的二维ECG数据压缩算法

本研究针对心电数据的压缩问题,提出了一种新的基于小波变换的二维心电（ECG）数据压缩算法。该算法首先将一维原始ECG信号转化为二维序列信号,从而使ECG数据的两种相关性可得到充分地利用;然后对二维ECG序列进行小波变换,并对变换后的系数应用了一种改进的矢量量化（VQ）方法。在改进的VQ方法中,根据小波变换后系数的特点,构造了一种新的树矢量（TV）。利用本算法与已有基于小波变换的压缩算法和其他二维ECG信号的压缩算法,对MIT/BIH数据库中的心律不齐数据进行了对比压缩实验。结果表明：本算法适用于各种波形特征的ECG信号,并且在保证压缩质量的前提下,可以获得较大的压缩比,具有一定的应用价值。     

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.     

基于心电信号提取呼吸信号的算法研究

目的:为了实现医用心电监护仪器对多种参数的检测,减少设备的复杂性和降低患者的不适感,基于呼吸运动对心电信号影响的理论依据,提出一种从心电信号提取呼吸信息新算法。方法:运用PanTompkins检测心电信号的R波和S波特征点,先后利用三次样条插值法和重采样法分别对此两路特征点进行处理,得到在相同位置采样拟合的R波序列和S波序列,选用小波变换理论重构一路呼吸信号序列,最后将处理得到的R波序列、S波序列、重构的呼吸信号序列和原信号4路信号序列构成混合矩阵,经独立分量分析(ICA)方法分离得到两路包含呼吸信息的源信号序列(Z1序列和Z2序列)。运用MATLAB软件对该算法的处理结果进行验证,并与相关的研究方法相比较。结果:在时域上对比统计人体每分钟呼吸次数,误差较小。经ICA方法提取出的两路源信号序列与其它呼吸信号波形有着良好的相关性,其平均相似度达到95.94%以上。结论:本研究提出的心电信号算法能够满足呼吸参数检测的需求,该算法是有效的。     

基于小波包变换的癫痫心电信号特征提取

目的:针对癫痫病的检测,从脑电中获取癫痫特征是传统的方法,但是,心电与脑电相结合的诊断方式是未来医疗卫生事业的重要发展方向,所以利用心电信号表征癫痫信息是一个值得研究的课题。方法:小波包变换为心电信号提供了一种十分精细的分析方法,它实现了信号能量在等宽频带上的分解。首先对单周期样本心电信号进行多层小波包分解,重构各个结点的分解系数并提取结点的能量;然后运用最小二乘法对结点能量值进行十次曲线拟合,并提取曲线中的能量极大值点。结果:在0 Hz到0.65 Hz频带内,癫痫心电样本的能量极大值点的频率位置集中在四个特征频带内,而其它心电样本的能量极大值点大部分分布在这四个频带范围以外,这为癫痫病的检测提供了良好的分类特征,实验结果表明本文算法对癫痫病具有较高的识别率。结论:心电信号易于检测且硬件成本低,在医疗中的应用十分频繁,本文算法能够方便的从心电信号中获取癫痫信息,这为癫痫病的检测与诊断提供了一条十分实用的途径。     

基于小波变换的R波检测算法

介绍了一种将小波变换应用于ECG信号检测R波的算法。该算法主要是在离散小波变换和多分辨率分析原理的基础上，利用db1小波特有的时频域特性，运用Mallat快速算法对心电信号进行了3层小波分解，分别在2、3层小波分解的高频系数中选择一定的阈值作为R波的判定条件，实现R波检测。通过对MIT／BIH（Massachusetts Institute of Technology／Boston’s Beth Israel Hospital）心电数据库的R波检测，结果表明，该检测算法即使在噪声干扰和病态的情况下，也很容易实现对R波的准确检测和精确定位，具有相当高的定位精度，R波正确检测率高达到99．8％。     

基于小波包变换与自适应阈值的ECG信号滤波算法研究

目的:在心电信号(ECG)的采集过程中,常常会受到噪声的影响,为了正确进行心电参数测量、波形识别和病情诊断,在低信噪微弱信号检测中必须要进行噪声抑制,提高信噪比.噪声的滤波处理是心电图分析的一个重要步骤.方法:本文提出了一种基于小波包变换及与分解层次相关的自适应阈值的去噪方法,利用小波包对心电信号进行分解,可以同时对信...     

基于小波变换算法的12导联同步心电图计算机辅助波形测量标定系统

研究了基于小波变换的心电波形识别算法，设计了12导联同步心电图计算机辅助分析系统，方便了临床医生对心电图波形的测量和标定。提高了心电图计算机自动识别的准确率和正检测率。     

Research of fetal ECG extraction using wavelet analysis and adaptive filtering

Extracting clean fetal electrocardiogram (ECG) signals is very important in fetal monitoring. In this paper, we proposed a new method for fetal ECG extraction based on wavelet analysis, the least mean square (LMS) adaptive filtering algorithm, and the spatially selective noise filtration (SSNF) algorithm. First, abdominal signals and thoracic signals were processed by stationary wavelet transform (SWT), and the wavelet coefficients at each scale were obtained. For each scale, the detail coefficients were processed by the LMS algorithm. The coefficient of the abdominal signal was taken as the original input of the LMS adaptive filtering system, and the coefficient of the thoracic signal as the reference input. Then, correlations of the processed wavelet coefficients were computed. The threshold was set and noise components were removed with the SSNF algorithm. Finally, the processed wavelet coefficients were reconstructed by inverse SWT to obtain fetal ECG. Twenty cases of simulated data and 12 cases of clinical data were used. Experimental results showed that the proposed method outperforms the LMS algorithm: (1) it shows improvement in case of superposition R-peaks of fetal ECG and maternal ECG; (2) noise disturbance is eliminated by incorporating the SSNF algorithm and the extracted waveform is more stable; and (3) the performance is proven quantitatively by SNR calculation. The results indicated that the proposed algorithm can be used for extracting fetal ECG from abdominal signals.     

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.     

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.     

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.