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％。     

多导同步心电图的QRS波检测及起止点的确定

本文采用从单导到多导的检测方法,首先利用小波变换实现单导ＱＲＳ波的检测,在此基础上,利用位置相关法进行多导ＱＲＳ波的检测,并利用心电信号的２＾１迟度小波变换的平方值来确定ＱＲＳ波的起止点,经过大量数据的检测证明取得了很好的效果。     

心率变化对心电信号各波间期的影响分析

分析了运动心电信号心率变化对其各特征波形的持续时间的影响程度,其中受影响最大的是ＴＰ间期,其次是ＱＴ间期,Ｐ波和ＱＲＳ波群的宽度基本不变,并根据ＱＴ间期的变化特点给出了运动心电关键检测参数ＳＴ段值的自适应调整Ｊ点后取值位置,对于准确地识别运动心电信号的各特征波和判定运动试验的结果具有重要意义。     

用小波变换对ECG进行特征提取——一种在线数字信号处理系统

本文介绍了一种使用小波变换方法进行心电图信号的特征提取的方法，主要是通过对ＱＲＳ复波进行准确的时间间隔测量来实现。本文提出的小波变换方法克服了其它方法存在的局限性，它能更精确地探测ＱＲＳ复波以及Ｐ波、Ｔ波的出现与停止。本方法用ＴＭＳ３２０Ｃ２５数字信号处理芯片来实时实现。文中介绍了其软件、硬件及其实验结果与分析。     

用小波变换对ECG进行特征提取：一种在线数字信号处理系统

本文介绍了一种使用小波变换方法进行心电图信号的特征提取的方法，主要通过对ＱＲＳ复波进行准确的时间间隔测量来实现。本文提出的小波变换方法克服了其它方法存在的局限性，它能更精确地探测ＱＲＳ复波以及Ｐ波、Ｔ波的出现与停止。本方法用ＴＭＳ３２０Ｃ２５数字信号处理芯片来实时实现。文中介绍了其软件、硬件及其实验结果与分析。     

兔心电QRS波的三维频谱分析

应用小波转换的三维频谱分析技术,对４０只兔心电ＱＲＳ波进行分析,观察其三维心电频谱的特征。结果显示：正常兔心电ＱＲＳ波的三维频谱形态呈双侧对称的山坡状,频谱宽度约为２４０Ｈｚ,高频成分主要分布在ＱＲＳ波的中部,能幅随频率增加呈下降趋势,其中１００—１０００Ｈｚ与６０—１００Ｈｚ的能量比约为１０,１５０—２５０Ｈｚ与６０—１００Ｈｚ的能量比约为０３５,并证明性别和体重对心电ＱＲＳ波三维频谱各参数不产生严重影响。     

QRS波群功率谱与心率、QRS波宽、QRS峰-峰值相关性的研究

本文分别对２０个人和２１只小鼠各作为一个群体以及将２０个人和２１只小鼠合在一起作为一个群体,研究了ＱＲＳ波群功率谱与心率、ＱＲＳ波宽、ＱＲＳ峰－峰值的相关性（本文只分析了Ⅱ、ａＶＦ、Ｖ３导联）。发现人、鼠各作为一个群体时,ＱＲＳ波群的１００－１０００Ｈｚ、８０－３００Ｈｚ的相对能量及绝对能量,０－１０００Ｈｚ的总能量与心率、ＱＲＳ波宽、ＱＲＳ波群的峰－峰值之间的相关关系,在不同导联中,无明显的规律性。而当人、鼠合在一起作为一个群体时,ＱＲＳ波群功率谱中１００－１０００Ｈｚ、８０－３００Ｈｚ的相对能量和绝对能量都与心率呈显著正相关（Ｐ＜０．０１）,而０－１０００Ｈｚ总能量与心率呈显著负相关（Ｐ＜０．０１）;ＱＲＳ波群功率谱中１００－１０００Ｈｚ、８０－３００Ｈｚ的相对能量和绝对能量都与ＱＲＳ波宽呈显著负相关（Ｐ＜０．０１）,而０－１０００Ｈｚ总能量与ＱＲＳ波宽呈正相关（Ｐ＜０．０５）;１００－１０００Ｈｚ、８０－３００Ｈｚ的相对能量、绝对能量和０－１０００Ｈｚ总能量与ＱＲＳ波群的峰－峰值均呈正相关。     

小波变换在心电图QRS波检测中的应用

作者利用信号的小波变换在多尺度边沿上的综合特性，提出了一种新的ＱＲＳ波检测法。有要用Ｍａｌｌａｔ快速算法获得原始ＥＣＧ信号在不同尺度上小波分解信号，将含有大部分高频ＱＲＳ波在多尺度上的分解信号送和一个线性自适应匹配滤波器，匹配滤波器的输出用于检测Ｒ波的位置。对ＭＩＴ数据库中的数据进行了检测，Ｒ波的检测率可达９９．８％。     

ECG自动分析技术的发展

本文对ＥＣＧ主要特征参数的自动分析技术进行了综述。在ＱＲＳ波检测中，介绍了基本信号处理的方法、基于图象识别的方法以及最近的子波变换、神经网络方法。同时介绍了Ｐ波、Ｔ波和ＳＴ段检测中的主要方法与存在的问题。     

ECG自动分析技术的发展

本对ＥＣＧ主要特征参数的自动分析技术进行了综述。在ＱＲＳ波检测中，介绍了基本信号处理的方法、基于图象识别的方法以及最近的子波变换、神经网络方法。同时介绍了Ｐ波、Ｔ波和ＳＴ段检测中的主要方法与存在的问题。     

心电信号QRS波的识别算法及程序设计

实现心电图QRS波检测的算法有很多,本文介绍了一种算法,即利用波变换的多尺特性,可以将QRS波从高P波,高T波,噪声,基线漂移和伪迹中分离出灵,并采用Microsoft VisualC 5.0编程实现算法,使用该方法对MIT／BIH心电数据库中带有严重基线漂移和噪声的心电信号进行处理,对QRS的识别率高达99．8％,文中给出给程序设计要点和程序流程图。     

Wavelet based ST-segment analysis

A novel algorithm for ST-segment analysis is developed using the multi-resolution wavelet approach. The system detects the QRS complexes and analyses each beat using the wavelet transform to identify the characteristic points (fiducial points). These fiducial points are, iso-electric level, the J point, and onsets and offsets of the QRS complex and T wave. The algorithm determines the T onset by looking for a point of inflection between the J point and the T peak. Furthermore, detection of characteristic points by the wavelet technique reduces the effect of noise. The results show that the proposed approach gives very accurate ST levels, as compared to the conventional (empirical) technique, at higher heart rates and with different morphologies. The algorithm detects the ST-segment length in 92.3% beats with an error of 4 ms, and in 97.3% beats the error is within 8 ms. The algorithm has been implemented on a TMS320C25 based add-on DSP card connected to a PC to provide the on-line analysis and display of ST-segment data.     

基于数学形态学方法的心电图波形分离技术

讨论了一种基于数学形态学的心电图波形分离方法。使用这种方法,无须检测QRS波群,利用一系列形态学运算,便可以直接去除心电信号中的QRS波群,检出P波和T波的起止点,实现波形的定性和定量分离。定性分离效果甚佳,定量分离结果的方差较小。此外,心电信号的滤波、基线矫正等处理,也完全由类似的形态学算法实现。     

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%.     

Feature extraction of ECG signal

This paper deals with new approaches to analyse electrocardiogram (ECG) signals for extracting useful diagnostic features. Initially, elimination of different types of noise is carried out using maximal overlap discrete wavelet transform (MODWT) and universal thresholding. Next, R-peak fiducial points are detected from these noise free ECG signals using discrete wavelet transform along with thresholding. Then, extraction of other features, viz., Q waves, S waves, P waves, T waves, P wave onset and offset points, T wave onset and offset points, QRS onset and offset points are identified using some rule based algorithms. Eventually, other important features are computed using the above extracted features. The software developed for this purpose has been validated by extensive testing of ECG signals acquired from the MIT-BIH database. The resulting signals and tabular results illustrate the performance of the proposed method. The sensitivity, predictivity and error of beat detection are 99.98%, 99.97% and 0.05%, respectively. The performance of the proposed beat detection method is compared to other existing techniques, which shows that the proposed method is superior to other methods.     

小波变换在心电信号特征提取中的应用

采用分段阈值和模极大值对斜率判据相结合的补偿策略,提出了一种精确提取QRS波群特征值的算法.经过对MIT/BIH心电数据库和临床实测的心电信号的大量实验,结果显示即使在有严重噪声干扰的情况下,运用本算法也很容易实现对QRS波群特征的有效提取,特别是对R波峰具有相当高的定位精度(其误差不超过一个采样点)和分析精度(没有累积误差).     

The use of the Hilbert transform in ECG signal analysis

This paper presents a new robust algorithm for QRS detection using the first differential of the ECG signal and its Hilbert transformed data to locate the R wave peaks in the ECG waveform. Using this method, the differentiation of R waves from large, peaked T and P waves is achieved with a high degree of accuracy. In addition, problems with baseline drift, motion artifacts and muscular noise are minimised. The performance of the algorithm was tested using standard ECG waveform records from the MIT-BITH Arrhythmia database. An average detection rate of 99.87%, a sensitivity (Se) of 99.94% and a positive prediction (+P) of 99.93% have been achieved against study records from the MIT-BITH Arrhythmia database. A detection error rate of less than 0.8% was achieved in every study case. The reliability of the proposed detector compares very favorably with published results for other QRS detectors.     

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.