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.     

基于独立元分析和联合小波熵检测多导联ECG信号的QRS

QRS波群的准确定位是ECG信号自动分析的基础。为提高QRS检测率,提出一种基于独立元分析(ICA)和联合小波熵(CWS)检测多导联ECG信号QRS的算法。ICA算法从滤波后的多导联ECG信号中分离出对应心室活动的独立元;然后对各独立元进行连续小波变换(CWT),重构小波系数的相空间,结合相空间中的QRS信息对独立元排序;最后检测排序后独立元的CWS得到QRS信息。实验对St.Petersburg12导联心率失常数据库及64导联犬心外膜数据库测试,比较本文算法与单导联QRS检测算法和双导联QRS检测算法的性能。结果表明,该文算法的性能最好,检测准确率分别为99.98%和100%。     

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.     

基于复值小波分解的QRS波群检测算法

心电信号特征参数的提取和识别是心电图分析和诊断的基础。在心电信号的分析中,QRS波群快速准确的检测非常重要,它是相关参数计算和诊断的前提。本文对心电信号进行复值小波分解后,利用分解结果的模值来检测QRS波。由于心电信号的形态和幅值因人而异,所以用自学习算法来调整阈值以适应信号的变化。用MIT-BIH心电数据库中的数据对以上方法进行验证,QRS波群的检测率高达99.81%以上。最后,在检测出QRS波群特征点的基础上,利用相类似的方法检测出P、T波。     

基于差分方法的QRS波检测

我们提出了一种基于差分方法的QRS波检测方法.该方法通过计算心电信号的差分函数,消除或减弱P波T波以及其它干扰信号对QRS波检测的影响,解决了传统检测方法检测准确率低和小波检测算法复杂,计算量大的问题.在对多组来自MIT-BIH数据库数据的实验结果表明,该方法对于各种病态波形具有很好的适应性.在我们研制的"十二导心电图PC系统"中,该方法已得到了临床应用,检测准确率达到99%以上.     

基于角度与幅值特征的QRS波群起止点识别

为了准确快速识别不同形态心电图(ECG)信号QRS波群的起止点,本文采用三次局部变换找出可能的起止点,然后再根据这些疑似起止点之间构成的角度与幅值特征,按由试验得出的规则流程从疑似起止点中选出正确的QRS波群的起止点。该方法充分利用了QRS波群的角度与幅值特征,只需做简单的运算就能得到结果,具有快速准确且对不同形态ECG信号适应性强的特点。利用MIT-BIH心电图数据库对该方法性能进行测试,取得了较好的效果。     

基于DCT的心电信号分类算法

目的提高心电信号的分类准确率,降低算法复杂度。方法首先以MIT-BIH心电数据作为学习模板,然后在心电信号的频域和时域上提取其离散余弦变换(discrete cosine transform,DCT)、RR间期和QRS复合波的三种特征值进行分析,最后采用最小欧式距离分类器判断待测心电信号的类型。结果该分类模型通过MIT-BIH和AHA国际标准心电数据库的验证,分别得到96.6%和94.1%的分类准确率。结论本文的心电分类模型区别于其他分类算法的一个最大特点就是算法复杂度低,这是异常心律能够被实时检测和预警的关键,而且建立的心电分类模型已经能够在普通的手机平台上实现。     

一种双导联融合心电QRS波检测算法

目的:心电图的检测与分析是临床上诊断心血管疾病的主要依据,QRS波是心电信号中最重要的特征波,它的准确检测是心电信号自动分析的前提和基础。为了提高单导联检测QRS波的灵敏度和准确率,本文提出一种新的双导联融合心电QRS波检测的算法。方法:原始心电信号通过单导联预检波进行QRS波定位后,由双导联决策方法来决定采用单导联检波还是双导联融合检波。单导联检波直接采用第一或第二导联检测结果;双导联融合检波由双导联融合方法和导联判断规则判别。以窗时间为时间单位,不断更新双导联决策算法。本算法包括方差、幅值、模板匹配以及阈值比较等方法。结果:采用MIT-BIH心律失常数据库的48组两导联心电记录进行验证,统计得到平均灵敏度和准确率分别为99.87%、99.81%。其漏检数和误检数比第一导联分别降低了23.26%、18.27%,比第二导联分别降低了88.21%、95.11%。结论:本算法实时高效地提高单导联QRS波检测的灵敏度和准确率,且优于部分算法的检测结果,因而在心电信号自动分析中具有良好的应用前景和较高的实用价值。     

基于小波变换的QRS波检测

目的　将小波变换应用于ECG信号QRS波检测,提高QRS波的正确检测率。方法　利用二进Marr小波对ECG信号按Mallat算法进行变换;从等效滤波器的角度分析了信号奇异点(R波峰值点)与其小波变换模极大值的关系;探讨二次微分小波与一次微分小波在奇异点分析时性能上的差异,在检测中还运用了一系列策略以增强算法的抗干扰能力。结果　经MIT/BIH标准心律失常数据库验证,QRS波的正确检测率高达99.8%。结论　小波技术在ECG信号消噪和精确定位显示良好的性能;不同的小波函数直接影响结果和后续的检测策略。     

T波检测和QT间期提取策略

提出一种T波检测和QT间期提取新策略,应用QRS波群起始点和终末点检测算法,检测到QRS波群的起始点和终末点;从QRS波群的终末点出发,向后求出16点线段参数的LS估计;根据LS估计确定窗口,在窗口内检测出T波的峰谷值位置,从而检出T波;从峰谷值位置向后根据LS估计确定R点和R回归直线,根据心电数据和R回归直线在R点前的偏离程度确定T波终末点,从而提取QT问期.应用具有广泛认可度的MIT-BIH数据库中QT数据库的所有具有T波终末点专家标记的数据文件来验证算法,在专家标记终末点的3 542个T波上获得98.2%的检出率,提取QT间期获得1.0 ms的平均误差,提取QT间期的准确率为97.2%.     

Detection of the QRS complex by linear prediction

The electrocardiogram (ECG) represents the electrical activity of the heart. It is characterized by its recurrent or periodic behaviour with each beat. Each recurrence is composed of a wave sequence consisting of P, QRS and T-waves, where the most characteristic wave set is the QRS complex. In this paper, we have developed an algorithm for detection of the QRS complex. The algorithm consists of several steps: signal-to-noise enhancement, linear prediction for ECG signal analysis, nonlinear transform, moving window integrator, centre-clipping transformation and QRS detection. Linear prediction determines the coefficients of a forward linear predictor by minimizing the prediction error by a least-square approach. The residual error signal obtained after processing by the linear prediction algorithm has very significant properties which will be used to localize and detect QRS complexes. The detection algorithm is tested on ECG signals from the universal MIT-BIH arrhythmia database and compared with the Pan and Tompkins QRS detection method. The results we obtain show that our method performs better than this method. Our algorithm results in fewer false positives and fewer false negatives.     

Detection of the QRS complex by linear prediction

The electrocardiogram (ECG) represents the electrical activity of the heart. It is characterized by its recurrent or periodic behaviour with each beat. Each recurrence is composed of a wave sequence consisting of P, QRS and T-waves, where the most characteristic wave set is the QRS complex. In this paper, we have developed an algorithm for detection of the QRS complex. The algorithm consists of several steps: signal-to-noise enhancement, linear prediction for ECG signal analysis, nonlinear transform, moving window integrator, centre-clipping transformation and QRS detection. Linear prediction determines the coefficients of a forward linear predictor by minimizing the prediction error by a least-square approach. The residual error signal obtained after processing by the linear prediction algorithm has very significant properties which will be used to localize and detect QRS complexes. The detection algorithm is tested on ECG signals from the universal MIT-BIH arrhythmia database and compared with the Pan and Tompkins QRS detection method. The results we obtain show that our method performs better than this method. Our algorithm results in fewer false positives and fewer false negatives.     

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%.     

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%.     

An innovative approach of QRS segmentation based on first-derivative, Hilbert and Wavelet Transforms

The QRS detection and segmentation processes constitute the first stages of a greater process, e.g., electrocardiogram (ECG) feature extraction. Their accuracy is a prerequisite to a satisfactory performance of the P and T wave segmentation, and also to the reliability of the heart rate variability analysis. This work presents an innovative approach of QRS detection and segmentation and the detailed results of the proposed algorithm based on First-Derivative, Hilbert and Wavelet Transforms, adaptive threshold and an approach of surface indicator. The method combines the adaptive threshold, Hilbert and Wavelet Transforms techniques, avoiding the whole ECG signal preprocessing. After each QRS detection, the computation of an indicator related to the area covered by the QRS complex envelope provides the detection of the QRS onset and offset. The QRS detection proposed technique is evaluated based on the well-known MIT-BIH Arrhythmia and QT databases, obtaining the average sensitivity of 99.15% and the positive predictability of 99.18% for the first database, and 99.75% and 99.65%, respectively, for the second one. The QRS segmentation approach is evaluated on the annotated QT database with the average segmentation errors of 2.85±9.90ms and 2.83±12.26ms for QRS onset and offset, respectively. Those results demonstrate the accuracy of the developed algorithm for a wide variety of QRS morphology and the adaptation of the algorithm parameters to the existing QRS morphological variations within a single record.     

基于模糊推理的心拍分类

心拍分类对于临床心律失常自动化检测非常重要。临床上对心拍分类的诊断标准存在一定的不确定性,模糊推理可以较好地表达心拍分类过程中的不确定性,而隶属度函数的设计是模糊推理系统的关键问题。本研究提取较为精确的QRS复合波间期和RR间期特征组成模糊输入量;通过对MIT-BIH心律失常心电数据库的所有正常拍和室性早搏模糊输入量进行统计分析,提出了一种设计隶属度函数的具体思路,并实现了一个用于心拍分类的模糊推理系统。通过对MIT-BIH心律失常心电数据库测试,该系统心拍分类结果较好,具有临床应用价值。     

基于心电图QRS波群的心肌缺血时域分析

目的 ST-T段变化是心电图检测心肌缺血主要的临床表现,代表了心室复极的电位变化;但其特征点定位存在很大的不准确性,为了克服这一难点,本研究从心电图QRS波群出发进行心肌缺血分析.方法 从心电图QRS波群(代表了心室的除极过程)出发,综合提取QRS波群的各个时域参数,然后进行心肌缺血与非心肌缺血条件下的统计检验.结果 ...     

基于卷积神经网络特征提取与融合的心律失常分类

本研究提出一种新的心律失常自动分类方法,辅助医生诊治心律失常。通过构建卷积神经网络对心电信号以及QRS波群的小波分量进行特征提取,将网络提取到的心电信号特征和小波特征与人工提取的RR间期特征,输入到全连接层进行融合,在输出层使用softmax函数对心拍进行分类。使用MIT-BIH心律失常数据库中的MILL导联数据对网络进行训练和测试。经测试,该方法的总体分类准确度达98.12%,平均灵敏度为87.32%,平均阳性预测值为90.37%。该方法能够快速识别不同类型的心律失常,对于计算机辅助诊断心律失常的应用具有一定的参考价值。     

A new arrhythmia clustering technique based on Ant Colony Optimization

In this paper, a new method for clustering analysis of QRS complexes is proposed. We present an efficient Arrhythmia Clustering and Detection algorithm based on medical experiment and Ant Colony Optimization technique for QRS complex. The algorithm has been developed based on not only the general signal detection knowledge, but also on the ECG signal’s specific features. Furthermore, our study brings the power of Ant Colony Optimization technique to the ECG clustering area. ACO-based clustering technique has also been improved using nearest neighborhood interpolation. At the beginning of our algorithm, we implement signal filtering, baseline wandering and parameter extraction procedures. Next is the learning phase which consists of clustering the QRS complexes based on the Ant Colony Optimization technique. A Neural Network algorithm is developed in parallel to verify and measure the success of our novel algorithm. The last stage is the testing phase to control the efficiency and correctness of the algorithm. The method is tested with MIT-BIH database to classify six different arrhythmia types of vital importance. These are normal sinus rhythm, premature ventricular contraction (PVC), atrial premature contraction (APC), right bundle branch block, ventricular fusion and fusion. Our simulation results indicate that this new approach has correctness and speed improvements.