Detection of the electrocardiogram fiducial points in the phase space using the Euclidian distance measure

The paper proposes a phase-space based algorithm applying the Euclidian distance measure enabling detection of heartbeats and characteristic (fiducial) points from a single-lead electrocardiogram (ECG) signal. It extends the QRS detection in the phase space by detecting the P and T fiducial points. The algorithm is derived by reconstructing the ECG signals in a two-dimensional (2D) phase space according to the delay method and utilizes geometrical properties of the reconstructed phase portrait of the signal in the phase space for the heartbeat and fiducial-point detection. It uses adaptive thresholding and the Euclidian distance measure between the signal points in the phase portrait as an alternative to the phase-portrait area calculation (Lee et al., 2002). It was verified with the QT Database (2011) and its performance was assessed using sensitivity (Se) and the positive predictive value (PPV). Results for the proposed algorithm are 99.06%, 99.75% and 99.66% for Se and 94.87%, 99.75% and 99.66% for PPV for the P points, heartbeats and T points, respectively.     

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。     

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.     

A new arrhythmia clustering technique based on Ant Colony Optimization

In this paper, a new method for clustering analysis of QRS complexes is proposed. We present an efficient Arrhythmia Clustering and Detection algorithm based on medical experiment and Ant Colony Optimization technique for QRS complex. The algorithm has been developed based on not only the general signal detection knowledge, but also on the ECG signal’s specific features. Furthermore, our study brings the power of Ant Colony Optimization technique to the ECG clustering area. ACO-based clustering technique has also been improved using nearest neighborhood interpolation. At the beginning of our algorithm, we implement signal filtering, baseline wandering and parameter extraction procedures. Next is the learning phase which consists of clustering the QRS complexes based on the Ant Colony Optimization technique. A Neural Network algorithm is developed in parallel to verify and measure the success of our novel algorithm. The last stage is the testing phase to control the efficiency and correctness of the algorithm. The method is tested with MIT-BIH database to classify six different arrhythmia types of vital importance. These are normal sinus rhythm, premature ventricular contraction (PVC), atrial premature contraction (APC), right bundle branch block, ventricular fusion and fusion. Our simulation results indicate that this new approach has correctness and speed improvements.     

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.     

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%.     

New approach to studies on ECG dynamics: Extraction and analyses of QRS complex irregularity time series

How to extract information intensively from ECGs for the diagnosis of cardiovascular diseases and assessment of heart function is a topical subject. Using a method based on the wavelet transform to calculate the irregularity of the QRS complex, which may relate to inotropy, the QRS complex irregularity time series is successfully extracted from original ECG signals. This provides a new approach to studies of ECG dynamics. With the help of non-linear dynamics theory, the QRS complex irregularity time series of eight subjects, from the MIT/BIH arrhythmia database are studied qualitatively and quantitatively, and the characteristics of ECG dynamics are analysed extensively. The power spectrum, phase portrait, correlation dimension, largest Lyapunov exponent, time-dependent divergence exponent and complexity measure all verify the fact that ECG dynamics are dominated by an underiying 5–6-dimensional non-linear chaotic system, whose complexity measure is about 0.7. The QRS complex irregularity time series contains abundant information about all parts of the heart and the regulation of the autonomic nervous system, and so further analyses are of great potential theoretical and clinical significance to patho-physiology studies and ambulatory monitoring.     

基于小波变换的QRS波检测

目的　将小波变换应用于ECG信号QRS波检测,提高QRS波的正确检测率。方法　利用二进Marr小波对ECG信号按Mallat算法进行变换;从等效滤波器的角度分析了信号奇异点(R波峰值点)与其小波变换模极大值的关系;探讨二次微分小波与一次微分小波在奇异点分析时性能上的差异,在检测中还运用了一系列策略以增强算法的抗干扰能力。结果　经MIT/BIH标准心律失常数据库验证,QRS波的正确检测率高达99.8%。结论　小波技术在ECG信号消噪和精确定位显示良好的性能;不同的小波函数直接影响结果和后续的检测策略。     

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).     

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。     

Combined entropy based method for detection of QRS complexes in 12-lead electrocardiogram using SVM

A method based on signal entropy is proposed for the detection of QRS complexes in the 12-lead electrocardiogram (ECG) using support vector machine (SVM). Digital filtering techniques are used to remove power line interference and base line wander in the ECG signal. Combined Entropy criterion was used to enhance the QRS complexes. SVM is used as a classifier to delineate QRS and non-QRS regions. The performance of the proposed algorithm was tested using 12-lead real ECG recordings from the standard CSE ECG database. The numerical results indicated that the algorithm achieved 99.93% of detection rate. The percentage of false positive and false negative is 0.54% and 0.06%, respectively. The proposed algorithm performs better as compared with published results of other QRS detectors tested on the same database.     

结合QRS检测和小波阈值的膈肌肌电信号降噪方法

为了消去夹杂在膈肌肌电(EMGdi)信号中的心电干扰,在比例阈值算法的基础上,提出一种结合QRS检测和小波阈值的降噪方法.首先,根据小波系数的相关性构造QRS波群的检测方法,分析确定干扰的位置和范围;其次,将小波系数分为受干扰和未受干扰两部分,并构造相应的阈值算法,针对性地处理受干扰系数,以未受干扰部分系数作为阈值算法构造的依据;最后,重构处理后的小波系数,得到降噪后的EMGdi信号.对临床采集信号的处理对比表明,该方法能够更为有效地去除心电干扰,并更好地保留EMGdi的有用信号.     

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.     

用于ECG信号检测与重建的双正交样条小波滤波器

本文依据双通道滤波器组的理想重建议程。设计一组双正交样条小波滤波器,实现了ECG信号的小波分解、完全重建和去噪重建,并把它应用于ECG信号的R波检测,得到了较好的结果。经MIT/BIH标准心电数据库检测验证,R波正确检测率可达99.62%。     

Die Spektralanalyse des EKG zur Erkennung von Spätpotentialen als Marker für die Gefährdung durch bedrohliche Rhythmusstörungen des Herzens

Summary Late potentials in the terminal phase of the QRS and early S-T segment are looked upon as a risk marker in patients prone to sustained ventricular tachycardia after myocardial infarction. Since the amplitude of late potentials at the body surface is very low (1–5 V), most studies use signal averaging of the ECG to increase the signal-to-noise ratio. Two different approaches are generally used to analyze the signal-averaged ECG. In the time domain, the individual channels are combined into a vector magnitude and highpass filtered in a bidirectional mode. Late potentials are suspected if the filtered QRS duration is >120 ms and/or the amplitude in the terminal 40 ms of the QRS complex is 25 V. The limitations of this method are that the definition of abnormality differs from one study group to another, highpass filters may introduce artificial signals, patients with bundle branch block in general have to be excluded, and the definitions depend upon the noise level.More recently, spectral analysis of the ECG with Fast Fourier Transform (FFT) has been performed. Late potentials are characterized by a higher frequency content in the otherwise low-frequent S-T wave. We analyzed 25 overlapping segments of the terminal QRS and early S-T wave time shifted in steps of 2 ms with FFT (spectrotemporal mapping). This method was shown to overcome some of the limitations of conventional time domain analysis: no highpass filters have to be applied, noise interference can be detected by a characteristic spectral pattern, and patients with bundle branch block need not be excluded. In this retrospective study spectrotemporal mapping was abnormal in 26/38 patients (67%) after myocardial infarction with sustained ventricular tachycardia.Only 3/21 patients after myocardial infarction without ventricular tachycardia had abnormal values. In healthy persons an abnormal Fourier result is a rare finding.Thus, spectral analysis of the ECG might offer promise for an improved identification of patients prone to sustained ventricular tachycardia after myocardial infarction.

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Abkürzungsverzeichnis FFT schnelle Fourier Transformation - RMS Amplitude mittlere Amplitude in den terminalen 40 ms des QRS Komplexes in V Herrn Professor Dr. F. Scheler zum 65. Geburtstag gewidmet.     

基于小波变换的小鼠QRS复合波检测

采用二进小波变换与斜率和幅度相结合的方法，对小鼠QRS复合波进行检测。根据小鼠QRS复合波的特点，采用Daubechics小波为母函烽，按照ECG的频谱特点选用尺度因子，对有噪声污染和形态变异的QRS复合波进行了检测。结果表明：小波变换对小鼠QRS复合波的检测是一种有效的方法。     

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.     

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.     

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。