基于形态小波的QRS波检测算法

根据心电信号中QRS波群的特点,提出了一种基于小波变换和数学形态学相结合的形态小波检测算法。小波变换方法对突变信号在时频域都具有优异的辨识能力及“可变焦距”的优良特性;数学形态学是基于信号局部特征的,能够在时域上提取信号的峰谷信息。将这两种方法结合起来,利用MIT/BIH心电数据库进行验证,QRS波群的检出率高达99.84%。     

基于香农能量与自适应阈值的心电QRS复合波检测算法

针对现有心电QRS复合波检测算法对于一些信号异常的情况检测效果仍然不理想的问题,提出了一种基于香农能量与自适应阈值相结合的心电QRS复合波检测算法,以解决QRS复合波检测的低准确率问题。首先,从预处理后的信号提取香农能量包络;然后,结合改进的自适应阈值方法对QRS复合波进行检测;最后,根据QRS复合波增强后的信号定位所检测的QRS复合波的位置。使用MIT-BIH心律失常数据库的数据对所提算法进行性能评估,结果表明,所提算法即使在信号中存在高大的P波、T波、不规则心律以及严重的噪声干扰时依然能准确检测QRS复合波的位置,总体数据检测的敏感性、阳性检测度和准确率分别达到了99.88%、99.85%和99.73%,且该算法能够在保证准确率的情况下快速地完成QRS复合波的检测任务。     

基于数学形态滤波和提升小波变换的R波检测

利用数学形态学与提升小波变换相结合的方法对心电信号进行分析处理。先用数学形态方法对心电信号进行滤波预处理,可以有效地去除高频白噪声与低频的基线飘移,再利用提升小波变换对处理后的心电信号进行多分辨分析,得出各层逼近信号与细节信号,并在此基础上结合不应期、自适应阈值和回溯检漏等方法,提出了一种动态的R波检测算法,使得QRS波群的检出率达到99.89%。     

卡尔曼滤波在心电信号QRS波检测中的尝试

心电信号QRS波的检测方法很多，但在准确性与实时性方面都不太好，该文中将心电信号按照QRS波周期进行分割，利用RLS算法的自适应AR建模，为心电信号建立模型，再利用kalman滤波算法对心电信号进行滤波和预测，在保证R波探测率的同时提高了探测的速度。针对心率不齐或者QRS波周期产生波动的情况，程序中利用各QRS波周期的相似性，求其互相关，以确定周期T，同时对T进行自适应建模，以便对下一周期预测。经过试验，取得了比较好的效果。     

mexican-hat小波在QRS波检测中的应用研究

提出了一种基于小波变换,结合多种判别修正方法的QRS波检测算法。首先采用mexican-hat小波对信号进行小波变换,在第3尺度上采用模极大值阈值法对R波进行检测。其次采用平面几何的数学方法定位Q波和S波。对于高尖P波和大T波造成的误检,采用弧度法进行纠正。对于高频干扰造成的影响用QRS时长法予以排除。该算法经过MIT-BIH Arrythmia Database的心电数据验证,取得了满意的结果。     

基于小波变换与形态学运算的R波检测算法

本文提出了一种基于小波变换与形态学运算的R 波检测算法。采用二进Marr小波的Mallat算法对心电信号作多分辨率分解，利用数学形态学运算突出信号的峰谷点特征，将小波变换模极大值检测原理与形态学峰谷检测算法相结合，不仅可以实现对 R 波的准确检测和精确定位，同时也具有较好的算法实时性。     

移动心电监护系统的心电图实时分析算法

在移动心电监护系统中,心电图中QRS波群的实时检测以及心律失常分析是至关重要的问题.针对这一问题,首先采用Pan-Tompkins算法检测QRS波,区分正常心电信号和心律失常,然后采用基于粗粒化过程的Lempel-Ziv(LZ)复杂度算法对心律失常进行分析.通过对MIT-BIH数据库中的100条正常心电信号、120条心动过速信号和60条心室颤动信号进行仿真测试,结果表明该算法能够克服各种噪声对心电信号的影响,实现QRS波的精准检测,而且基于K-Mean粗粒化过程的LZ复杂度算法可以有效分离心动过速和心室颤动,是一种分析心律失常比较实用的方法.     

面向智能服装健康监护系统的心电信号基线漂移处理*

面向智能服装的健康监护系统心电信号存在严重的基线漂移,针对基漂去除的需要,提出了基于基线漂移阈值的分级处理方法。首先采用滑动窗口中值滤波算法对心电信号进行滤波,并计算出基线漂移的程度大小,当其大于给定的阈值时,采用小波变换得到QRS波群的位置信息和信号的特点来变动滑动窗口大小。中值滤波和小波算法可以在两个处理平台上并行运行,提高了运算速度;最后,运用该算法分别对模拟和实际的基线漂移进行处理,并与其他算法的处理结果进行了比较,结果表明该算法具有较好的实时性和处理效果。     

小波包分析和形态学滤波相结合的心电图QRS波群定位

心电图波形特征提取是针对-维心电信号的弱信号特征提取.如何排除各种干扰,提取出心电波形特征,并准确定位心电信号中P波、QRS波群、T波,一直是心脏病智能诊断的难点和热点问题,其中QRS波群的定位又是其它波定位的重要依据.利用形态学和小波包理论相结合的方法对这一问题进行了探讨,提出了QRS波群定位和滤除基线漂移的方法.实验证明提出的方法速度较快,能较准确的定位QRS波群、有效的去除基线漂移.     

基于多孔算法的心电图QRS波检测

关于心电图检测,由于受到噪声干扰影响,检测不准确.针对目前对QRS波群的起止点和R波峰值点检测定位不精确问题,提出一种多孔算法的特征点定位检测法.通过利用三次B样条小波的高阶平滑特性最大限度的去除噪声干扰,提取准确波形;利用多孔算法对不规则离散信号的无抽取平移不变性保证采样信号的完整性,在小波变换的过程中精确定位QRS波群的起止点和R波峰值点,实验结果表明,准确率达到99.80％.算法增强了对复杂突变心电信号的检测结果,对QRS波群的起止点和R波峰值点检测定位的准确性.     

QRS complexes detection for ECG signal: the Difference Operation Method

This paper proposes a simple and reliable method termed the Difference Operation Method (DOM) to detect the QRS complex of an electrocardiogram (ECG) signal. The proposed DOM includes two stages. The first stage is to find the point R by applying the difference equation operation to an ECG signal. The second stage looks for the points Q and S based on the point R to find the QRS complex. From the QRS complex, the T wave and P wave can be obtained by the existing methods. Some records (QRS complex and T and P waves) of ECG signals in MIT-BIH arrhythmia database is tested to show the DOM has a much more precise detection rate and faster speed than other methods.     

Motion artifact removal based on periodical property for ECG monitoring with wearable systems

Motion artifact removal (MR) is one of the essential issues in processing raw ECG signals since it could not be simply solved by using classic filtering. In this paper, a QRS detection based Motion Artifact Removal algorithm (QRSMR) is proposed. The proposed method detects the entire QRS complex and removes the noise between two QRS complexes, while recovering P and T-waves. As verified in the tests on simulated noisy ECG signals, QRSMR outputs with seriously contaminated ECG signals have an increase of the correlation with their original clean signals from 40% to nearly 80%, demonstrating the improved noise removal ability of QRSMR. Moreover, in the tests on real ECG signals measured on volunteers with a flexible wearable ECG monitoring device developed at Fudan University, QRSMR is able to recover P-wave and T-wave from the contaminated signal, which shows its enhanced performance on motion artifact reduction comparing with adaptive filtering method and other methods based only on empirical mode decomposition.     

Development of QRS detection algorithm designed for wearable cardiorespiratory system

An in-home sleep monitoring system was developed previously in our laboratory for monitoring electrocardiography (ECG) and respiratory signals. However, the ECG signal acquired with this system is prone to high-grade noise caused by motion artifact. Since the detection of the QRS complexes with high accuracy is very important in a computer-based analysis of the ECG, a high accuracy QRS detection algorithm is developed and based on the combination of heart rate indicators and morphological ECG features. The proposed algorithm is tested both on 16 h data acquired using the two sensors of our cardiorespiratory belt system, i.e., the polyvinylidene fluoride (PVDF) film and the conductive fabric sheets, and on all 48 records of the MIT/BIH Arrhythmia Database. Satisfying results are obtained for both databases, the sensitivity S(e) and positive predictivity P(+) were calculated for each case and results show S(e)=[96.98%, 93.76%] and P(+)=[97.81%, 99.48%] for conductive fabric and PVDF film sensors, respectively, and S(e)=99.77% and P(+)=99.64% in the case of the MIT/BIH Arrhythmia Database. Further, heart rate variability (HRV) measures were calculated using our system and a commercial system. A comparison between systems' results is done to show the usefulness of our developed algorithm used with our cardiorespiratory belt sensor.     

QRS detection based on wavelet coefficients

Electrocardiogram (ECG) signal processing and analysis provide crucial information about functional status of the heart. The QRS complex represents the most important component within the ECG signal. Its detection is the first step of all kinds of automatic feature extraction. QRS detector must be able to detect a large number of different QRS morphologies. This paper examines the use of wavelet detail coefficients for the accurate detection of different QRS morphologies in ECG. Our method is based on the power spectrum of QRS complexes in different energy levels since it differs from normal beats to abnormal ones. This property is used to discriminate between true beats (normal and abnormal) and false beats. Significant performance enhancement is observed when the proposed approach is tested with the MIT-BIH arrhythmia database (MITDB). The obtained results show a sensitivity of 99.64% and a positive predictivity of 99.82%.     

一种多导联QRS波实时检测算法

针对多导联心电监护仪对QRS波的分析需求,提出一种多导联QRS波实时检测算法。对原始心电图信号进行工频滤波和低通滤波处理,将各导联按照单导联预检波规则进行QRS波判别,通过决策融合多个导联的判别结果得到最终判别结果。在圣彼得堡INCART 12导联心率失常数据库上的验证结果表明,该算法的平均识别率和准确率分别为99.88%和99.73%。     

ECG waveform analysis by significant point extraction. II. Pattern matching

From a set of significant points which characterizes the ECG waveform, the pattern matching algorithm detects and classifies QRS complexes. R waves are detected from the analysis of global curvature. Next, the morphology of the QRS complex is determined. QRS complexes with different morphologies are classified by a correlation algorithm. This method is sensitive to changes in shape, such as that of abnormal QRS complexes. The algorithm should be useful in automated analysis of waveforms, such as ECG signals recorded in clinical environments.     

基于第二代小波变换的心电信号分析算法

为了改进用于计算机辅助诊断的心电信号处理中QRS组波检测速度以及实现心电信号的精确重构，本文提出利用第二代小波变换即提升格式对心电信号进行处理的方法。采用双正交样条小波滤波器，与此同时，给出提升方案。为了验证方法的实效性，对美国MIT-BIT数据库中的几组心电信号进行了初步的处理与试验分析，结果表明该方法不仅改善了检测速度和重构的精确性，同时也为心电信号的压缩处理提供了方便。     

Detecting variabilities of ECG signals by Lyapunov exponents

An approach based on the consideration that electrocardiogram (ECG) signals are chaotic signals was presented for automated diagnosis of electrocardiographic changes. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. Multilayer perceptron neural network (MLPNN) architectures were formulated and used as basis for detection of variabilities of ECG signals. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database were classified. The computed Lyapunov exponents of the ECG signals were used as inputs of the MLPNNs trained with backpropagation, delta-bar-delta, extended delta-bar-delta, quick propagation, and Levenberg–Marquardt algorithms. The performances of the MLPNN classifiers were evaluated in terms of classification accuracies. The results confirmed that the MLPNN trained with the Levenberg–Marquardt algorithm has potential in detecting the variabilities of the ECG signals (total classification accuracy was 95.00%).