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%。     

Identification of QRS complexes in 12-lead electrocardiogram

A method for the detection of QRS complexes in 12-lead electrocardiogram (ECG) using support vector machine (SVM) is presented in this paper. Digital filtering techniques are used to remove base line wander and power line interference. SVM is used for the identification of QRS complexes in the processed signal. The performance of the algorithm is evaluated against the standard CSE ECG database. The results indicated that the algorithm achieved 99.75% of the identification rate. The percentage of false positive and false negative is 1.61% and 0.26%, respectively. The performance of the proposed algorithm is found to be better than published results of the other QRS detectors tested on the same database. The proposed method functions reliably even under the conditions of poor signal quality in the ECG data.     

心电信号中R波的小波包检测算法研究

心电信号(ECG)特征参数的提取和检测是心电图分析和诊断的基础,而在ECG分析中,快速准确地检测出QRS波群非常重要,它是计算相关参数和诊断的前提。本文针对心电信号QRS波群中R波的检测算法进行了研究与分析,对传统的小波R波检测算法中存在的检测准确率较低和实时性较差的问题加以改进,并提出了具有更加优良分析效果的小波包R波检测方法。并利用美国麻省理工学院的MIT/BIH心电数据库对上述方法进行仿真验证,同时与传统的小波R波检测算法进行实验对比,结果表明,该方法简单有效、准确率高,适于实际应用。     

心电R波检测及控制左心辅助装置

心衰是一种威胁人类生命的主要疾病之一,左心室辅助装置作为心脏移植前的过渡支持治疗使用,对心衰终末期患者的生活质量和生存率有明显提升;在对心电图实现自动的检测中,QRS波群的检测是最关键的环节,它影响着后续数据分析和处理的正确性及准确性;设计利用数据库数据和人体心电采集数据作为原始待处理源,通过高阶低通滤波与香农能量的创新算法结合进行处理数据,在硬件上增加使用高阶自适应中值滤波算法,使得处理后的实时数据噪声降到最低,有利于准确提取到R波,最后使用测试后的心电R波驱动左心室辅助装置,达到对心衰患者进行辅助治疗的作用,并且通过MATLAB和Modelsim软件仿真成功后,在FPGA硬件上实现了实时心电信号控制血泵系统的方案;经试验测试此方法可以准确的提取R波,并且可以控制左心辅助装置同步进行泵血。     

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。     

基于非线性能量算子的心电信号R波检测方法

将Marr小波变换和非线性能量算子相结合实现了心电信号的R波检测,心电信号的Marr小波分解信号很好地抑制了各种噪声干扰,结合非线性能量算子运算可突出了QRS波的特征点,使得阈值检测便于实施,利用修正策略提高了R波检测率,经MIT/BIH标准心律失常数据库验证,R波的检测率可达到99.7%,该方法对于心电信号的自动分析系统具有应用价值。     

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.     

QRS complex detection using Empirical Mode Decomposition

In this paper, we present a new Empirical Mode Decomposition based algorithm for the purpose of QRS complex detection. This algorithm requires the following stages: a high-pass filter, signal Empirical Mode Decomposition, a nonlinear transform, an integration and finally, a low-pass filter is used. In order to evaluate the proposed technique, the well known ECG MIT–BIH database has been used. Moreover it is compared to a reference technique, namely “Christov's” detection method. As it will be shown later, the proposed algorithm allows to achieve high detection performances, described by means both the sensitivity and the specificity parameters.     

心电信号的aTrous小波变换综合检测方法

心电信号反映了心脏有节律的活动。R波、P波和T波是去、复极时产生的突变信号,是典型的峰值奇异信号。信号的突变点检测是小波变换应用的一个重要方面。确定QRS波群的具体形态和起止点,检测P波、T波特征点是心电图分析的难点。研究了信号的二进样条小波按aTrous（多孔）算法进行的变换,构建了系列检测方法,来检测和识别QRS波群、P波、T波的具体的形态和位置。实验结果表明,所提出的综合算法具有较好的适应性,能很好地抑制基线漂移,消除高频干扰,克服了大T波、大S波、高U波波形自身病态因素对综合检测产生的影响。     

不可逆电消融手术中心电同步算法及系统设计

随着不可逆电脉冲穿孔消融手术等疗法的发展,传统的心电信号同步设备从精确度、抗干扰能力以及安全性的角度无法满足要求。针对此问题,提出了适用于不可逆电消融手术的心电同步算法,包括一种区间可预测性条件和一种消融区间起点精确标定算法。在满足可预测条件的情况下对消融区间起点进行精确标定,实现在强干扰条件下的心电信号同步。经过实时心电信号和MIT-BIH心律失常数据库的验证,表明系统和算法具有较高的精度和可靠性,并可在嵌入式系统上实现。     

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%.     

小波滤波与QRS波检测

利用双正交样条小波等效滤波器,实现了ECG信号的小波分解和重建。分析心电信号奇异点与其小波变换模极大值对的零交叉点的关系,提出了心电信号QRS波检测的算法。在检测算法中还使用了一系列策略来提高算法的抗干扰能力和QRS检测的准确性。经MIT/BIH心律失常数据库验证,QRS波的正确检测率达99.506%。最后将该算法应用到Windows Mobile智能手机上的心电监护系统中,达到令人满意的效果。     

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.     

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.     

An efficient new method for the detection of QRS in electrocardiogram

A simple and efficient new method for QRS detection in Electrocardiogram (ECG) is proposed in this paper. Initially data is preprocessed using two stage median filter for removing baseline drift. The second stage enhances the peaks of ECG wave components by using sixth power of signal. The next stage identifies the QRS complex by taking a variable window size. The detection sensitivity (Se) and positive predictivity (+P) of CSE (Common Standards for Quantitative Electrocardiography) measurement database, MIT/BIH (Massachusetts Institute of Technology/Beth Israel Hospital) Arrhythmia database, European ST-T database and QT database are Se 99.51 & +P 99.69%, Se 99.21 & +P 99.34%, Se 99.53 & +P 99.72% and Se 99.87 & +P 99.95% respectively. These four standard databases used to perform QRS detection considered 368 cases, tested 1,006,168 beats and achieved overall average sensitivity 99.52% and positive predictivity 99.69%. The MIT/BIH Noise Stress Test Database also tested by proposed method.     

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。     

基于希尔伯特变换和自适应阈值的R波检测算法

提出一种基于希尔伯特变换和自适应双阈值的R波检测算法。首先对预处理后的信号进行幅度归一化和希尔伯特包络分析;然后采用自适应双阈值法检测R波;最后,根据增强后的信号定位检测到R波的位置。使用4个具有不同频率和信噪比的数据库(MIT-BIH心率失常数据库、QT数据库、NST噪声数据库、European ST-T数据库)和临床采集心电数据对所提算法进行性能评估,结果表明,各种不规律和含有严重噪声干扰的心电信号中R波的位置依然能被所提算法准确检测出。在MIT-BIH心律失常数据库中,总体数据检测的敏感性、阳性检测度和准确率分别达到了99.36%、99.77%和99.13%,每条记录平均消耗时间比传统的Pan and Tompkins算法大大缩短。实验结果表明该算法具有良好的鲁棒性和实时性。     

一种基于特征增强的自适应阈值R波检测算法

为提高心电信号R波检测准确度,提出了一种基于特征增强的R波检测算法。首先经过小波分析去除心电信号的高频噪声和基线漂移,然后对信号进行差分运算,选取各数据点前后连续10个差分的最大和最小值做乘积运算,达到增强R波特征的目的,最后设定两个自适应阈值,对全部数据完成检测。实验结果:经过MIT-BIH Arrhythmia Database数据验证,R波检测准确度Acc可达99.57%,敏感度Se高达99.76%,真阳性率+P高达99.82%。将得到的结果与已有文献中的方法进行比较,本文算法简单,实时性好,检测准确率高,更符合实际临床应用的需求。