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。     

基于变分模态分解的心电信号QRS波检测

为了解决含噪心电信号QRS波的提取问题,本研究提出了一种基于变分模态分解(variation mode decomposition,VMD)的心电信号QRS波群检测和定位的方法。首先确定合适的分解层数,利用变分模态分解将心电信号分解为一系列模态分量。对每层模态分量进行分析,选取含有QRS波的模态分量层。通过小波变换的奇异值检测原理,确定心电信号的奇异值,定位心电信号R波的峰值位置,再检测QRS波的波形宽度。实验证明了该方法对含噪的QRS波检测准确度在96%以上,能够准确的检测和定位心电信号QRS波。     

QRS复合波检测技术综述

心电图是诊断各种心脏疾病的一个重要手段,而准确识别QRS复合波也是多种自动化心电图分析方法的一个前 提。检测QRS复合波的传统方法主要有差分阈值算法、双阈值检测算法、经验模态分解法、小波变换算法等,这些算法的 主要步骤包括对心电信号进行预处理、特征提取和检测等,对心电信号质量要求比较高,且通用性不是很强。相对于传统 方法检测QRS复合波,人工智能的发展特别是深度学习的出现为QRS复合波检测提供一种新的方法,利用深度学习可自 主提取QRS复合波特征信息,从而进行精准定位,相比传统方法,鲁棒性更好,对信号质量不佳的数据检测效果更好。本 研究主要对用于QRS复合波预处理以及检测的技术进行综述,并对检测技术的发展进行展望。     

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。     

基于小波变换的心电信号中QRS复合波内异常高频分量的检测

提出利用小波变换方法提取心电信号中异常QRS复合波内的高频分量，并定义了残余信号的QRS，作为衡量指标，为检测QRS复合波内的异常高频分量提供了有效的定量检测方法。     

基于子波多尺度分辨的心电QRS波分类方法的研究

本文分析了心电QRS波的子波多尺度分辨特征,探讨了曲线非线性分开维数的计算方法,提出了一种新的QRS波的分类方法：对心电QRS复合波进行子波多尺度分解,在尺度为4的情况下,根据局部正负极大值对检测出它们前后两个零点Zp1,Zp2,计算出局部正负极大值对位于┃Zp1,Zp┃之间的曲线段的分形维数。根据局部正负极大值对的幅度和分开维数能很好地检出正常心电信号的QRS波及早搏信号;该方法具有很强的抗噪能力,提高了QRS波的正确检出率。     

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%.     

基于Ⅰ导联ECG的心肌梗死特征提取EI北大核心CSCD

提出了基于ECG导联Ⅰ的单周期信号的心肌梗死特征提取算法,避免了利用多导联ECG检测心肌梗死带来的不便。首先对导联Ⅰ的ECG信号进行去噪处理;然后,引入小波包算法提取QRS波群、T波的主频带,重构QRS波群、T波的波形并确定ST段的始末位置;最后,运用小波的多分辨分析对ST段进行分解并提取导联Ⅰ信号的心肌梗死的特征波形。实验结果表明,本文算法具有较高识别率,这为ECG导联Ⅰ信号用于心肌梗死的检测与诊断提供了依据。     

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。     

自动分析心电监护仪的QRS复合波检出方法

本文介绍两种自动分析心电监护仪的QRS复合波检出方法。一种基于带通滤波器的电子线路能有效地抑制T波和肌电干扰,去除基线漂移,正确检出R波。另一种计算心电信号的第一、二、三价微分,进而得到QRS宽度脉冲。两者所得输出信号都可送微型计算机作心律失常的自动分析。     

Empirical mode decomposition based ECG enhancement and QRS detection

In this paper an Empirical Mode Decomposition (EMD) based ECG signal enhancement and QRS detection algorithm is proposed. Being a non-invasive measurement, ECG is prone to various high and low frequency noises causing baseline wander and power line interference, which act as a source of error in QRS and other feature extraction. EMD is a fully adaptive signal decomposition technique that generates Intrinsic Mode Functions (IMF) as decomposition output. Here, first baseline wander is corrected by selective reconstruction based slope minimization technique from IMFs and then high frequency noise is removed by eliminating a noisy set of lower order IMFs with a statistical peak correction as high frequency noise elimination is accompanied by peak deformation of sharp characteristic waves. Then a set of IMFs are selected that represents QRS region and a nonlinear transformation is done for QRS enhancement. This improves detection accuracy, which is represented in the result section. Thus in this method a single fold processing of each signal is required unlike other conventional techniques.     

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.     

便携式心电监护系统中心电信号的实时分析方法设计

目的：设计并实现一种适用于便携式心电监护系统的心电波形实时动态检测和分析的方法。方法 ：作者首先应用5点平滑滤波消除信号的高频噪声和50 Hz干扰,然后通过对分段心电信号的长度变换来增强R波,并用长度阈值检测到R波位置,再通过去错检和查漏检算法提高R波检测准确率;正确检测到R波后,利用区域极值和斜率突变特点从R波开始向前、向后搜索找出Q、S波,然后从已开始的Q、S波位置再分别向前向后找到Q波起点和S波终点;最后根据已检测到的QRS波群计算了心率和ST段参数。结果：通过对包含各种噪声的心电信号的分析证明该算法能准确地检测到QRS波群,不受基线漂移和高频噪声的影响;算法用C语言实现后在嵌入式心电监护系统中的应用也表明其处理速度完全满足移动设备的实时动态分析要求。结论：本文设计的心电波形识别方法算法简单、速度快、抗干扰能力强、准确率高,并成功应用于基于32位嵌入式系统的心电监护仪。相信能给便携式心电监护设备研发中心电信号自动检测和分析功能的实现带来一些启发。     

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.     

Fast and reliable QRS alignment technique for high-frequency analysis of signal-averaged ECG

The process of QRS alignment as required in signal-averaged ECG can impose serious limitations on the spectral range of the signal output. This effect depends basically on the particular alignment technique being used and on the level and type of noise present in the recorded ECG. In clinical studies where a wide-band (1000 Hz) ECG averager is required, the conventional QRS alignment technique, based on maximum coherence matching (MCM) with a template beat, may not perform consistently well. An alternative QRS alignment technique based on the accurate detection of a single fiducial point (SFP) in the bandpass filtered (3–30 Hz) QRS complex was developed. Using computer simulation methods, a comparative assessment of the frequency bandwidths (3 dB points) offered by both MCM and SFP techniques as a function of noise level (15–100 μ RMS) and type (EMG and 50 Hz interference), was carried out. The results of the comparative assessment indicated a better performance by the SFP technique in all cases of noise. Hence, the SFP technique would perform more reliably for high-frequency analysis of a noisy ECG, especially when 50 Hz interference is high. Furthermore, SFP is considerably faster than MCM (about four times) when implemented digitially, and its analogue realisation is feasible. The SFP technique is suitable for late-potential analysis in the signal-averaged ECG.     

QRS detection using new wavelets

This paper deals with a new wavelet (WT) which has been developed and very effectively and efficiently used for the detection of QRS segments from the ECG signal. After carrying out the detection using five existing wavelets (two symmetric-- WT1 and WT2--and three asymmetric--WT3, WT4 and WT5), two new wavelets (WT6 and WT7) were constructed and used for QRS detection. WT6 is a symmetric wavelet and has been constructed by a trial-and-error method. WT7 is an adaptive symmetric wavelet and adjusts its threshold as per the amplitude of the ECG signal. The accuracy of QRS detection obtained from WT6 is 99.8% and from WT7 100%. The CSE DS-3 database has been used for tests. Both WT6 and WT7 have been proved to be superior in performance to the existing wavelets. Out of WT6 and WT7, WT7 holds high promise for error-free reliable QRS detection in computer-aided feature extraction and disease diagnostics.     

QRS detection using new wavelets

This paper deals with a new wavelet (WVT) which has been developed and very effectively and efficiently used for the detection of QRS segments from the ECG signal. After carrying out the detection using five existing wavelets (two symmetric--WT1 and WT2--and three asymmetric--WT3, WT4 and WT5), two new wavelets (WT6 and WT7) were constructed and used for QRS detection. WT6 is a symmetric wavelet and has been constructed by a trial-and-error method. WT7 is an adaptive symmetric wavelet and adjusts its threshold as per the amplitude of the ECG signal. The accuracy of QRS detection obtained from WT6 is 99.8 % and from WT7 100%. The CSE DS-3 database has been used for tests. Both WT6 and WT7 have been proved to be superior in performance to the existing wavelets. Out of WT6 and WT7, WT7 holds high promise for error-free reliable QRS detection in computer-aided feature extraction and disease diagnostics.