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%.     

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.     

嵌入式QT环境下的QRS波群实时检测与分析

目的 QRS波群的检测是心电图分析的核心技术之一.本文在嵌入式QT环境下实现了一套QRS波群实时检测与分析系统.方法 系统采用四点平均对ECG信号进行滤波,再对ECG信号的一二阶差分值进行平滑处理,然后在较短时间窗内实现QRS波的实时精确定位.最后采用MIT-BIH标准数据库对算法效果进行分析.结果 对于MIT-BIH标准数据库中的绝大部分心电数据,改进后的算法有96％以上的准确率,运行时间在1ms左右.结论 改进后的算法能够满足远程终端对准确率和运行时间的要求.     

A statistical approach for determination of time plane features from digitized ECG

This paper illustrates a method for time-plane feature extraction from digitized ECG sample using statistical approach. The algorithm detects the position and magnitude of the QRS complex, P and T wave for a single lead ECG dataset. The processing is broadly based on relative comparison of magnitude and slopes of ECG samples. Then the baseline modulation in the dataset is removed. The R-peak detection and baseline modulation is tested MIT-BIH arrhythmia database as well as 12-lead datasets in MIT-PTB database (PTBDB) and available under Physionet. The overall accuracy obtained is more than 99%.     

ECG beat classification using empirical mode decomposition and mixture of features

Computer-aided analysis is useful in predicting arrhythmia conditions of the heart by analysing the recorded ECG signals. In this work, we proposed a method to detect, extract informative features to classify six types of heartbeat of ECG signals obtained from the MIT-BIH Arrhythmia database. The powerful discrete wavelet transform (DWT) is used to eliminate different sources of noises. Empirical mode decomposition (EMD) with adaptive thresholding has been used to detect precise R-peaks and QRS complex. The significant features consists of temporal, morphological and statistical were extracted from the processed ECG signals and combined to form a set of features. This feature set is classified with probabilistic neural network (PNN) and radial basis function neural network (RBF-NN) to recognise the arrhythmia beats. The process achieved better result with sensitivity of 99.96%, and positive predictivity of 99.81 with error rate of 0.23% in detecting the QRS complex. In class-oriented scheme, the arrhythmia conditions are classified with accuracy of 99.54%, 99.89% using PNN and RBF-NN classifier respectively. The obtained result confirms the superiority of the proposed scheme compared to other published results cited in literature.     

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 ...     

一种基于经验小波变换的心电信号室性早搏检测算法

针对心电信号中的室性早搏心拍检测问题,使用经验小波变换（EWT）实现心电信号的自适应分解。根据心电信号时频能量变化特征,提出了一种低复杂度的频域累积能量特征计算方法,并分析了室性早搏与正常心电信号的特征差异性。最后利用反向传播神经网络在MIT-BIH心电数据库上进行心拍样本训练与识别测试。结果表明基于EWT的特征提取避免了传统时域特征提取中的QRS波群检测过程,降低了其它干扰因素对诊断结果的影响,具有较高的分类精度与良好的鲁棒性,总体敏感度与总体阳性检测率分别达到96.55%和97.73%。     

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。     

应用目标检测网络自动检测ECG信号所含噪声

现阶段的心电（ECG）信号去噪网络多通过滤波器滤除噪声,但是通常噪声滤除不彻底,从而造成ECG信号失真。基于此,本研究将目标检测网络用于ECG信号中的噪声检测,首先从MIT-BIT心律不齐数据库中筛选无噪声ECG记录,加入两种来自于MIT-BIH噪声压力测试数据库中的真实噪声,生成4个不同信噪比的含噪信号并构成训练及测试数据集。然后针对ECG信号的特殊性,对YOLOv3网络结构进行修改,设计YOLO-ECG目标检测网络,使用迁移学习策略训练目标检测网络。实验结果表明,本研究提出的网络在ECG信号中噪声检测时的F1值达0.955 8,具有良好的检测效果。     

Implementation of a Multi-sampling-frequency System for RR-interval Timing and Blood Pressure Detection

INTRODUCTION Researchers study on cardiac electrophysiology and haemodynamics for more effective methods of di-agnosis and treatment. During these studies, howto obtain information as much as possible, depends onnot only the progress of analysis methods, but also the improvement of signal acquisition and the condi-tions for further analysis. Without automatic data processing, the analysis of experimental data might bewith poor accuracy,time-consuming and even impossible in some new cardiov…     

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EMD与IMF分量统计特性的ECG去噪

经验模式分解（EMD）域内心电（ECG）信号的去噪,通常为基于QRS特征波经验性识别固有模态函数（IMF）分量并重建ECG信号。由于该方法引入个人误差,因此识别不准确。针对此问题,本文提出利用EMD与IMF分量统计特性对ECG信号进行去噪。本方法首先对含噪ECG信号进行EMD分解得到一系列IMF分量,然后利用IMF分量的统计特性识别IMF分量属性,并采用被识别为ECG信号的IMF分量重建ECG信号。该识别方法基于统计学方法,具有统计学和现实物理意义。将本方法应用于真实ECG信号去噪处理中,结果表明,本方法可有效去除ECG信号基线漂移噪声与肌电干扰噪声,去噪效果优于经验法。     

A new approach to QRS segmentation based on wavelet bases and adaptive threshold technique

In this paper, we develop and evaluate a new approach to QRS segmentation based on the combination of two techniques: wavelet bases and adaptive threshold. Firstly, QRS complexes are identified without a preprocessing stage. Then, each QRS is segmented by identifying the complex onset and offset. We evaluated the algorithm on two manually annotated databases, the QT-database and the MIT-BIH Arrhythmia database. The QRS detector obtained a sensitivity of 99.02% and a positive predictivity of 99.35% over the first lead of the validation databases (more than 192,000 beats), while for the QT-database, values larger than 99.6% were attained. As for the delineation of the QRS complex, the mean and the standard deviation of the differences between the automatic and the manual annotations were computed. Using QT-database which contains recordings of annotated ECG with a sampling rate of 250 Hz, we obtain the average of the differences not exceeding two sampling intervals, while the standard deviations were within acceptable range of values.     

基于经验模式分解的心电特征提取算法

本研究应用基于经验模式分解的心电特征提取方法,利用第一本征模函数(intrinsic mode function,IMF)分量对QRS波进行定位,并通过减少分解层数、筛选次数、处理区域等策略实现了快速算法。利用MIT-BIT心律失常数据库的数据进行算法测试,取得较高的检测率,检测速度也有明显提高。实验结果表明,经验模式分解算法在QRS波定位中具有相当的优越性,临床应用中取得了良好的检测效果。     

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。