Detection of QRS complexes in electrocardiogram using support vector machine

This paper presents the application of a support vector machine (SVM) for the detection of QRS complexes in the electrocardiogram (ECG). The ECG signal is filtered using digital filtering techniques to remove noise and baseline wander. The support vector machine is used as a classifier to delineate QRS and non-QRS regions. Two different algorithms are presented for the detection of QRS complexes. The first uses a single-lead ECG at a time for the detection of QRS complexes, while the second uses 12-lead simultaneously recorded ECG. Both algorithms have been tested on the standard CSE ECG database. A detection rate of 99.3% is achieved when tested using a single-lead ECG. This improves to 99.75% for the simultaneously recorded 12-lead ECG signal. The percentage of false negative detection is 0.7% and the percentage of false positive detection is 12.4% in the single-lead QRS detection and it reduces to 0.26% and 1.61% respectively for QRS detection in simultaneously recorded 12-lead ECG signals. The performance of the algorithms depends strongly on the selection and the variety of the ECGs included in the training set, data representation and the mathematical basis of the classifier.     

静息12导联心电图碎裂QRS波群诊断非ST段抬高型急性心肌梗死的价值

目的 探讨静息12导联心电图碎裂QRS波群诊断非ST段抬高型急性心肌梗死中价值。方法 回顾性分析我院2016年12月~2018年12月收治的104例非ST段抬高型急性心肌梗死患者的临床资料,按照静息12导联心电图检查结果将其分为碎裂QRS波群阴性组（n=34）和碎裂QRS波群阳性组（n=70）,比较两组超声心动图指标及实验室检查指标。结果 碎裂QRS波群阳性组左心室舒张末期内径、室壁瘤占比高于碎裂QRS波群阴性组,左室射血分数及室壁运动异常占比低于碎裂QRS波群阴性组（P＜0.05）;碎裂QRS波群阳性组肌酸激酶同工酶水平低于碎裂QRS波群阴性组,肌钙蛋白Ⅰ水平高于碎裂QRS波群阴性组（P＜0.05）。结论 静息12导联心电图碎裂QRS波群能够有效判断非ST段抬高型急性心肌梗死患者实验室检查水平、左心室舒张末期内径及左室射血分数,可为临床诊断及治疗提供有效参考依据。     

一种使用关联积分进行QRS波检测的新方法

使用计算机对ECG信号进行分析和处理能够减少医生的工作量,提高医生的工作效率.在ECG自动诊断系统中,对QRS波进行准确检测非常重要.目前,QRS波的检测方法已经有很多种,如使用差分和滤波器组、小波变换、神经网络等方法等,但还没有一种方法的适应性和准确性得到广泛的认同.关联积分的方法是从混沌学理论中推导出来,是一种全新的检测QRS波方法.使用关联积分的方法对心电信号QRS波进行分析,得到了很好的效果,而且这种分析方法具有较好的鲁棒性.本文利用MIT/BIH Arrhythmia Database库中的数据测试了上述方法,结果证实其对QRS波的正确检出率为99.1%.     

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 ...     

A real time QRS detection using delay-coordinate mapping for the microcontroller implementation

In this article, we propose a new algorithm using the characteristics of reconstructed phase portraits by delay-coordinate mapping utilizing lag rotundity for a real-time detection of QRS complexes in ECG signals. In reconstructing phase portrait the mapping parameters, time delay, and mapping dimension play important roles in shaping of portraits drawn in a new dimensional space. Experimentally, the optimal mapping time delay for detection of QRS complexes turned out to be 20 ms. To explore the meaning of this time delay and the proper mapping dimension, we applied a fill factor, mutual information, and autocorrelation function algorithm that were generally used to analyze the chaotic characteristics of sampled signals. From these results, we could find the fact that the performance of our proposed algorithms relied mainly on the geometrical property such as an area of the reconstructed phase portrait. For the real application, we applied our algorithm for designing a small cardiac event recorder. This system was to record patients ECG and R–R intervals for 1 h to investigate HRV characteristics of the patients who had vasovagal syncope symptom and for the evaluation, we implemented our algorithm in C language and applied to MIT/BIH arrhythmia database of 48 subjects. Our proposed algorithm achieved a 99.58% detection rate of QRS complexes. © 2002 Biomedical Engineering Society. PAC2002: 8719Nn, 8719Hh, 8780-y     

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.     

QRS波群时频检测方法的新进展

在心电信号的QRS波群检测算法中,最为有效的是把待测信号在时域或频域内进行适当的变换,以分离或加强QRS分量,抑制各种噪声干扰,再进行判决定位.简要回顾了以滤波器为代表的单一尺度时频变换方法、以小波分析为代表的多尺度时频变换方法,以及另一种较新的多尺度时频变换分析方法-经验模式分解（EMD）等在QRS波群检测中的应用情况和发展前景.     

QRS波群时频检测方法的新进展

在心电信号的QRS波群检测算法中,最为有效的是把待测信号在时域或频域内进行适当的变换,以分离或加强QRS分量,抑制各种噪声干扰,再进行判决定位。简要回顾了以滤波器为代表的单一尺度时频变换方法、以小波分析为代表的多尺度时频变换方法,以及另一种较新的多尺度时频变换分析方法—经验模式分解(EMD)等在QRS波群检测中的应用情况和发展前景。     

Wavelet based R-peak detection for heart rate variability studies

Detection of QRS complex in electrocardiogram (ECG) signals is of immense importance in cardiac health prognosis. In this paper a new symmetric wavelet for detection of R-peak is presented, which has been designed based on spectral characteristics and morphology of QRS complex. The detection of R-peak was carried out using this designed wavelet, and with existing symmetric wavelets such as db3, db6, haar and bior2.2. The detection accuracy with this wavelet is 99.99%, which is higher than those with existing symmetric wavelets. The algorithm has been tested on standard databases such as Fantasia database of normal and healthy subjects, MIT/BIH (Massachusetts Institute of Technology/Beth Israel Hospital) arrhythmia database, and on self-recorded electrocardiograms of normal subjects and patients under diseased stress. The study of heart rate variability (HRV) through computation of RR-tachogram using the new wavelet has proved to be effective in reliably evaluating HRV parameters.     

基于LS估计检测QRS波群宽度

提出一种检测心电图QRS波群宽度的新方法,在检测出QRS波群并确定任意一点在QRS波群内的基础上,以所确定的点为基点,向前和向后逐段求出线段参数的LS估计,并求出线段的线性度,根据线段参数的LS估计和线性度确定基线,在此基线上再利用假设检验的方法,得出QRS波群的起点和终点,从而提取了QRS波群宽度这一特征参数。应用具有广泛认可度的MIT-BIH数据库的QT数据库的所有105个数据文件验证算法,在第一组专家标记的3623个QRS波群上,平均误差为1.2ms;在第二组专家标记的404个QRS波群上,平均误差为2.1ms。该方法具有较强的抗噪声能力和抗基线漂移能力,计算量小,运行速度快,精度高,适于实时提取。     

A new approach to detect QRS complexes based on a histogram and genetic algorithm

A new approach, based on a histogram and genetic algorithm (GA), has been developed to rapidly and reliably detect QRS complexes of the ECG curve. Here, the new approach is described and the principle of the histogram and GA is explained. The results of detection of QRS complexes using the new method are given. Additionally, as an example of its novel application, the P-wave and f-wave (when atrial fibrillation is absent and present, respectively) are extracted easily and efficiently from the ECG curve using the method.     

Modeling of the heart's ventricular conduction system using fractal geometry: Spectral analysis of the QRS complex

Many biological systems having one or more characteristics that remain constant over a wide range of scales may be considered self-similar or fractal. Geometrical and functional overview of the ventricular conduction system of the heart reveals that it shares structures common to a tree with repeatedly bifurcating “branches,” decreasing in length with each generation. This system may further simplify by assuming that the bifurcating and decreasing process is the same at any generation, that is, the shortening factor and the angle of bifurcation are the same for each generation. Under these assumptions, the conduction system can be described as a fractal tree. A model of the heart's ventricles which consists of muscle cells and a fractal conduction system is described. The model is activated and the dipole potential generated by adjacent activated and resting cells is calculated to obtain a QRS complex. Analysis of the frequency spectrum of the QRS complex reveals that the simulated waveforms show an enhancement in the high frequency components as generations are added to the conduction system. It was also found that the QRS complex shows a form of an inverse power law, which was predicted by the fractal depolarization hypothesis, with a highly correlated straight line for a log-power versus log frequency plot with a slope of approximately −4. Similar results were obtained using real QRS data from healthy subjects.     

Evaluation of real-time QRS detection algorithms in variable contexts

A method is presented to evaluate the detection performance of real-time QRS detection algorithms to propose a strategy for the adaptive selection of QRS detectors, in variable signal contexts. Signal contexts are defined as different combinations of QRS morphologies and clinical noise. Four QRS detectors are compared in these contexts by means of a multivariate analysis. This evaluation strategy is general and can be easily extended to a larger number of detectors. A set of morphology contexts, corresponding to eight QRS morphologies (normal, PVC, premature atrial beat, paced beat, LBBB, fusion, RBBB, junctional premature beat), was extracted from 17 standard ECG records. For each morphology context, the set of extracted beats, ranging from 30 to 23000, was resampled to generate 50 realisations of 20 concatenated beats. These realisations were then used as input to the QRS detectors, without noise, and with three different types of additive clinical noise (electrode motion artifact, muscle artifact, baseline wander) at three signal-to-noise ratios (5 dB, −5 dB, −15 dB). Performance was assessed by the number of errors, which reflected both false alarms and missed beats. The results show that the evaluated detectors are indeed complementary. For example, the Pan-Tompkins detector is the best in most contexts but the Okada detector generates fewer errors in the presence of electrode motion artifact. These results will be particularly useful to the development of a real-time system that will be able to choose the best QRS detector according to the current context.     

QRS波群检测方法的新进展

QRS波群检测是心脏病诊断和心电监护的重要方法。本文较全面地阐述了近年来 QRS波群检测的几种实现的方法 ,并分析比较了各种方法的基本理论、实现过程和特点     

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％。     

Evaluation of a none compressed 12-lead ECG transmission via internet using IPSec

Electrocardiogram (ECG) transmission in real time could involve some problems with the quality of services and security. This paper aims to evaluate a none compressed 12-lead ECG transmission using the Secure Internet Protocol (IPSec), compared with the popular Internet Protocol (IP). Using an analytical model, the transmission performance is estimated in terms of end-to-end delay and loss rate. Our results show that ECG transmission could be assured both security and quality of services.     

Detection of QRS complexes in electrocardiogram using support vector machine

This paper presents the application of a support vector machine (SVM) for the detection of QRS complexes in the electrocardiogram (ECG). The ECG signal is filtered using digital filtering techniques to remove noise and baseline wander. The support vector machine is used as a classifier to delineate QRS and non-QRS regions. Two different algorithms are presented for the detection of QRS complexes. The first uses a single-lead ECG at a time for the detection of QRS complexes, while the second uses 12-lead simultaneously recorded ECG. Both algorithms have been tested on the standard CSE ECG database. A detection rate of 99.3% is achieved when tested using a single-lead ECG. This improves to 99.75% for the simultaneously recorded 12-lead ECG signal. The percentage of false negative detection is 0.7% and the percentage of false positive detection is 12.4% in the single-lead QRS detection and it reduces to 0.26% and 1.61% respectively for QRS detection in simultaneously recorded 12-lead ECG signals. The performance of the algorithms depends strongly on the selection and the variety of the ECGs included in the training set, data representation and the mathematical basis of the classifier.     

QRS complex detection based on simple robust 2-D pictorial-geometrical feature

In this paper a heuristic method aimed for detecting of QRS complexes without any pre-process was developed. All the methods developed in previous studies were used pre-process, the most novelty of this study was suggesting a simple method which did not need any pre-process. Toward this objective, a new simple 2-D geometrical feature space was extracted from the original electrocardiogram (ECG) signal. In this method, a sliding window was moved sample-by-sample on the pre-processed ECG signal. During each forward slide of the analysis window an artificial image was generated from the excerpted segment allocated in the window. Then, a geometrical feature extraction technique based on curve-length and angle of highest point was applied to each image for establishment of an appropriate feature space. Afterwards the K-Nearest Neighbors (KNN), Artificial Neural Network (ANN) and Adaptive Network Fuzzy Inference Systems (ANFIS) were designed and implemented to the ECG signal. The proposed methods were applied to DAY general hospital high resolution holter data. For detection of QRS complex the average values of sensitivity Se?=?99.93% and positive predictivity P+?=?99.92% were obtained.