心电信号的计算机分析—特征检测

心电信号的特征检测是心电计算机分析的核心内容,本文报导了作者采用的方法,包括Ｒ波定位,ＱＲＳ波群区分点检测,Ｐ波,Ｔ波区发点的检测,Ｕ波的检测以及Ｐ－Ｑ,Ｓ－Ｔ段的分析,测试结果表明这些方法的优越性和可靠性,在ＳＴ段分析上有较大突破。     

基于小波变换的QRS综合波的检测

本文描述了基于二进制小波变换（DyWT),ECG信号中QRS综合波的检测。设计－小波它适合于QRS检测,将基于心电信号的特殊的特征的特征为小波的尺度。DyWT较之其它方法最基本的优点为强有力的抑制噪声检测以及在分析随时间变化ECG波形时的灵活性。     

基于小波变换的心电信号去噪综合算法

目的：研究去除心电信号中的基线漂移、工频干扰和肌电干扰等噪声,提高心电信号的自动识别和诊断精度。方法：利用Coif4小波对心电信号进行8尺度分解,采用小波分解重构法去除基线漂移,然后利用改进的小波闽值算法去除工频干扰和肌电干扰。结果：利用Matlab仿真工具,选择MIT-BIH心率失常数据库中信号进行验证,能有效去除这三种噪声,并且很好的保持R波的信息。结论：本算法在不丢失心电信号有用信息的前提下,可以较好的去除三种常见的噪声,可以用于心电信号自动分析之前的预处理。     

一种心电波群实时检测的新算法

本文提出了一种新的ＱＲＳ波群检测方法－斜率匹配最大值检测法，该方法与现有的其他ＱＲＳ波群检测方法相比，具有算法简单，抗噪声性能好的特点。     

温度对密点麻蜥心电活动的影响(英文)

采用甘肃省民勤县荒漠半荒漠环境中的卵胎生蜥蜴密点麻蜥（Ｅｒｅｍｉａｓｍｕｔｉｏｃｅｌａｔａ）为材料,研究其心电活动随体温变化的规律以及对环境温度的适应特点。共记录密点麻蜥１２５只,每只蜥蜴记录５、１０、１５、２０、２５、３０、３５℃７个温度等级,每个等级１５～２０只;少数蜥蜴记录的温度范围扩展到４０、４２、４４、４５和４６℃。环境温度采用由电接点温度计和继电器控制的电冰箱和恒温箱来控制。体温测量采用ＳＹ－２型数字式温度计,测定时插入泄殖腔２ｃｍ。心电描记采用ＬＭＳ２Ｂ型二道生理记录仪。电极为不锈钢针形电极。实验前将蜥蜴放入待测温度环境中适应２ｈ。被测蜥蜴背位固定于木板上,不麻醉,将记录电极的正极插入左前肢皮下,负极插入右前肢皮下,地线插入后肢皮下,插入深度均为５ｍｍ。电极固定后待蜥蜴的体温达到预定温度５ｍｉｎ后再开始心电记录。在实验记录纸上测量各波的电压值及各间期的时间,其中Ｒ～Ｔ间期即Ｓ～Ｔ段,表示从ＱＲＳ波结束到Ｔ波结束的时间,Ｔ～Ｐ间期表示从Ｔ波结束到Ｐ波开始时的时间,Ｐ～Ｒ间期表示从Ｐ波开始到ＱＲＳ波开始的时间,以ｔ测验检验相关系数的显著性。体温为５～３５℃时的心电图中Ｐ波和Ｔ波是正向的,且幅度很     

幼狮心电图的测定和分析

本研究采用标准双极肢导联（Ｉ,Ⅱ,Ⅲ）和加压单极肢导联（ａＶＲ,ａＶＬ,ａＶＦ）,对未经麻醉并处于清醒安静状态下的４只全同胞幼狮进行了心电图测定。结果表明,幼狮的平均心率为１５９±１次／ｍｉｎ,均为窦性心律,ＱＲＳ波平均心电轴为６８±１８°。Ｐ波,ＱＲＳ综合波及Ｔ波持续时间分别为０．０５５±０．００４ｓ,０．０６７±０．００６ｓ和０．０６９±０．０１２ｓ。Ｒ－Ｒ,Ｐ－Ｒ,Ｓ－Ｔ及Ｑ－Ｔ间期分别为０．３７８±０．００２ｓ,０．０８２±０．００６ｓ,０．０７８±０．００５８和０．２２５±０．０１７ｓ。Ｐ波的形态在Ｉ,Ⅱ,Ⅲ和ａＶＦ导联中呈正向,而在ａＶＲ及ａＶＬ导联中呈负向;ＱＲＳ综合波的波形呈现一定的规律性,在Ｉ,Ⅱ,Ⅲ及ａＶＦ导联中主要表现为Ｒ型正向波,而在ａＶＲ,ａＶＬ导联中则表现为Ｑ或Ｓ型负向波;Ｓ－Ｔ段无出现移位现象;Ｔ波在ａＶＬ导联中,没有表现出明显的规律性,而在Ｉ,Ⅱ,Ⅲ和ａＶＦ导联中,主要呈正向,在ａＶＲ和ａＶＬ导联中则往往呈负向．     

正常小鼠高频心电图时域值和功率谱的研究

本文用南京新博公司生产的ＮＨＥ－１０００型心电高频信息检测分析仪研究了正常小鼠（昆明种）高频心电图（ＨＦ－ＥＣＧ）的时域值和ＱＲＳ波群的功率谱。主要结果如下（以正导为例,－Ｘ±ＳＤ）：心率６０３±８８次／ｍｉｎ（ｎ＝７４）;Ｐ－Ｒ间期相对较长。为３４．９±４．７ｍｓ（ｎ＝５８）,占心动周期的３４．９±４．９％,这与人类有很大的不同;ＱＲＳ波宽９．２±１．２ｍｓ,占心动周期的９．２±１．４％（ｎ＝７４）,这一结果与以前的文献报道相差较大。Ｔ波宽１０．３±３．２ｍｓ,占心动周期的１０．３±３．２％;Ｑ－Ｔ间期１９．４±３．２ｍｓ,占心动周期的１９．５±３．６％;ＱＲＳ波群峰－峰值（Ｖｐ－ｐ）为１．４５６±０．４８０ｍＶ;Ｔ波高０．３３６±０．１１５ｍＶ;７３只动物Ⅱ导联高频切迹总数只有３个,扭挫２６个。Ⅱ导联ＱＲＳ波群的功率谱特点：０—８０Ｈｚ的相对能量为４５．４８±１５．３２％;８０—２００Ｈｚ为４３．９７±９．９５％;２００—３００Ｈｚ为８．８９±７．３８％;３００—１０００Ｈｚ为１．６６±２．７４％。     

草原兔尾鼠心电图的分析

本文采用标准双极导联（Ⅰ、Ⅱ、Ⅲ）、加压单极肢导联（ａＶＲ、ａＶＬ、ａＶＦ）和单极胸导联（Ｖａ、Ｖｂ、Ｖｃ）,对经１０％乌拉坦麻醉状态下的１１９只成体草原兔尾鼠进行心电图测定,同时对２０只昆明系小白鼠作为对照比较。结果表明,草原兔尾鼠的平均心率为５５８．５４±５４．５９次／ｍｉｎ,均为窦性心率,额面心电轴平均５９．６１±１２．６３度。ＰＤ－Ｒ、ＱＲＳ波、Ｑ－Ｔ、Ｐ波平均间期分别为３８．２９±６．４８、１２．７０±０．４６、２７．６２±７．３７、１２．３４±４．０９毫秒。Ｐ波和Ｔ波的方向与主波Ｒ波一致。在Ⅰ、Ⅱ、ⅢａＶＬ、ａＶＦ和Ｖａ导联为正,在ａＶＲ导联均为负向。无典型的Ｓ－Ｔ段,ＱＲＳ波群与Ｔ波部分重叠,Ｑ－Ｔ间期较短。     

心电信号特征量的小波提取算法

目的:为了更准确地利用心电图(ECG)进行临床生理疾病诊断,提高心电信号的自动分析准确度.介绍了一种利用小波变换的时频局部化特性以及多分辨率特性对心电信号进行处理的算法.方法:使用定位准确.计算简便的二阶微分Mart小波使用多孔算法来对ECG中QRS波群进行标定.结果:将算法应用到MIT/BIH国际标准心电数据库进行仿真.结论:通过仿真证明,该算法能够很精确地定位QRS波群,为心电信号的后续研究打好基础.     

一种基于小波变换的心电去噪算法

目的:去除心电信号采集过程中混入的工频干扰、肌电干扰和基线漂移等噪声信号,并能有效的保留心电特征信息.方法:通过小波变换将含噪的心电信号分解并重构得到不同尺度下的细节信号,在中小尺度上选取不同的门限值,并在QRS波群信息多的尺度上计算获得信息窗,对该尺度的信息窗内外采用不同的门限处理方式,在大尺度上直接重构出要去除的基线信息.结果:采用MIT/BIH Arrhythmia Database中的数据对算法进行了仿真验证,实现了三种主要干扰的去除,较好的保留了心电特征信息.结论:本方法效果较好,为后续的特征点识别奠定了基础.     

基于独立元分析和联合小波熵检测多导联ECG信号的QRS

QRS波群的准确定位是ECG信号自动分析的基础。为提高QRS检测率,提出一种基于独立元分析（ICA）和联合小波熵（CWS）检测多导联ECG信号QRS的算法。ICA算法从滤波后的多导联ECG信号中分离出对应心室活动的独立元;然后对各独立元进行连续小波变换（CWT）,重构小波系数的相空间,结合相空间中的QRS信息对独立元排序;最后检测排序后独立元的CWS得到QRS信息。实验对St.Petersburg12导联心率失常数据库及64导联犬心外膜数据库测试,比较本文算法与单导联QRS检测算法和双导联QRS检测算法的性能。结果表明,该文算法的性能最好,检测准确率分别为99.98%和100%。     

一种QRS波群实时检测方法

结合模板匹配和改进的导数阈值法,提出了一种QRS波群实时检测方法CT2(combination method of template matching and improved derivative threshold)。首先,预采集一段ECG信号,使用高斯函数构造QRS模板;然后将实时采集的ECG信号使用CT2检测R波位置。为了比较算法检测精度和效率,使用CT2和基于小波模极大值的方法进行了对比。结果表明,CT2检测精度与基于小波模极大值的方法相当,但运算时间大大缩短,适于实时检测。     

Simple and Robust Realtime QRS Detection Algorithm Based on Spatiotemporal Characteristic of the QRS Complex

The purpose of this research is to develop an intuitive and robust realtime QRS detection algorithm based on the physiological characteristics of the electrocardiogram waveform. The proposed algorithm finds the QRS complex based on the dual criteria of the amplitude and duration of QRS complex. It consists of simple operations, such as a finite impulse response filter, differentiation or thresholding without complex and computational operations like a wavelet transformation. The QRS detection performance is evaluated by using both an MIT-BIH arrhythmia database and an AHA ECG database (a total of 435,700 beats). The sensitivity (SE) and positive predictivity value (PPV) were 99.85% and 99.86%, respectively. According to the database, the SE and PPV were 99.90% and 99.91% in the MIT-BIH database and 99.84% and 99.84% in the AHA database, respectively. The result of the noisy environment test using record 119 from the MIT-BIH database indicated that the proposed method was scarcely affected by noise above 5 dB SNR (SE = 100%, PPV > 98%) without the need for an additional de-noising or back searching process.     

Ischemia episode detection in ECG using kernel density estimation, support vector machine and feature selection

ABSTRACT: BACKGROUND: Myocardial ischemia can be developed into more serious diseases. Early Detection of the ischemic syndrome inelectrocardiogram (ECG) more accurately and automatically can prevent it from developing into a catastrophicdisease. To this end, we propose a new method, which employs wavelets and simple feature selection. METHODS: For training and testing, the European ST-T database is used, which is comprised of 367 ischemic ST episodes in90 records. We first remove baseline wandering, and detect time positions of QRS complexes by a method basedon the discrete wavelet transform. Next, for each heart beat, we extract three features which can be used fordifferentiating ST episodes from normal: 1) the area between QRS offset and T-peak points, 2) the normalizedand signed sum from QRS offset to effective zero voltage point, and 3) the slope from QRS onset to offset point.We average the feature values for successive five beats to reduce effects of outliers. Finally we apply classifiersto those features. RESULTS: We evaluated the algorithm by kernel density estimation (KDE) and support vector machine (SVM) methods.Sensitivity and specificity for KDE were 0.939 and 0.912, respectively. The KDE classifier detects 349 ischemicST episodes out of total 367 ST episodes. Sensitivity and specificity of SVM were 0.941 and 0.923, respectively.The SVM classifier detects 355 ischemic ST episodes. CONCLUSIONS: We proposed a new method for detecting ischemia in ECG. It contains signal processing techniques of removingbaseline wandering and detecting time positions of QRS complexes by discrete wavelet transform, and featureextraction from morphology of ECG waveforms explicitly. It was shown that the number of selected featureswere sufficient to discriminate ischemic ST episodes from the normal ones. We also showed how the proposedKDE classifier can automatically select kernel bandwidths, meaning that the algorithm does not require anynumerical values of the parameters to be supplied in advance. In the case of the SVM classifier, one has to selecta single parameter.     

A mathematical algorithm for ECG signal denoising using window analysis

BACKGROUND: The presence of parasite interference signals could cause serious problems in the registration of ECG signals and many works have been done to suppress electromyogram (EMG) artifacts noises and disturbances from electrocardiogram (ECG). Recently, new developed techniques based on global and local transforms have become popular such as wavelet shrinkage approaches (1995) and time-frequency dependent threshold (1998). Moreover, other techniques such as artificial neural networks (2003), energy thresholding and Gaussian kernels (2006) are used to improve previous works. This review summarizes windowed techniques of the concerned issue. METHODS AND RESULTS: We conducted a mathematical method based on two sets of information, which are dominant scale of QRS complexes and their domain. The task is proposed by using a varying-length window that is moving over the whole signals. Both the high frequency (noise) and low frequency (base-line wandering) removal tasks are evaluated for manually corrupted ECG signals and are validated for actual recorded ECG signals. CONCLUSIONS: Although, the simplicity of the method, fast implementation, and preservation of characteristics of ECG waves represent it as a suitable algorithm, there may be some difficulties due to pre-stage detection of QRS complexes and specification of algorithm's parameters for varying morphology cases.     

Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains

This paper presents a new ECG denoising approach based on noise reduction algorithms in empirical mode decomposition (EMD) and discrete wavelet transform (DWT) domains. Unlike the conventional EMD based ECG denoising approaches that neglect a number of initial intrinsic mode functions (IMFs) containing the QRS complex as well as noise, we propose to perform windowing in the EMD domain in order to reduce the noise from the initial IMFs instead of discarding them completely thus preserving the QRS complex and yielding a relatively cleaner ECG signal. The signal thus obtained is transformed in the DWT domain, where an adaptive soft thresholding based noise reduction algorithm is employed considering the advantageous properties of the DWT compared to that of the EMD in preserving the energy in the presence of noise and in reconstructing the original ECG signal with a better time resolution. Extensive simulations are carried out using the MIT-BIH arrythmia database and the performance of the proposed method is evaluated in terms of several standard metrics. The simulation results show that the proposed method is able to reduce noise from the noisy ECG signals more accurately and consistently in comparison to some of the stateof-the-art methods.     

Discrete-wavelet-transform-based noise removal and feature extraction for ECG signals

Nowadays, doctors use electrocardiogram (ECG) to diagnose heart diseases commonly. However, some nonideal effects are often distributed in ECG. Discrete wavelet transform (DWT) is efficient for nonstationary signal analysis. In this paper, the Symlets sym5 is chosen as the wavelet function to decompose recorded ECG signals for noise removal. Soft-thresholding method is then applied for feature detection. To detect ECG features, R peak of each heart beat is first detected, and the onset and offset of the QRS complex are then detected. Finally, the signal is reconstructed to remove high frequency interferences and applied with adaptive searching window and threshold to detect P and T waves. We use the MIT-BIH arrhythmia database for algorithm verification. For noise reduction, the SNR improvement is achieved at least 10 dB at SNR 5 dB, and most of the improvement SNR are better than other methods at least 1 dB at different SNR. When applying to the real portable ECG device, all R peaks can be detected when patients walk, run, or move at the speed below 9 km/h. The performance of delineation on database shows in our algorithm can achieve high sensitivity in detecting ECG features. The QRS detector attains a sensitivity over 99.94%, while detectors of P and T waves achieve 99.75% and 99.7%, respectively.     

Paroxysmal atrial fibrillation recognition based on multi-scale wavelet <Emphasis Type="Italic">α</Emphasis>-entropy

Background

This study proposed an effective method based on the wavelet multi-scale α-entropy features of heart rate variability (HRV) for the recognition of paroxysmal atrial fibrillation (PAF). This new algorithm combines wavelet decomposition and non-linear analysis methods. The PAF signal, the signal distant from PAF, and the normal sinus signals can be identified and distinguished by extracting the characteristic parameters from HRV signals and analyzing their quantification indexes. The original ECG signals for QRS detection and HRV signal extraction are first processed. The features from the HRV signals are extracted as feature vectors using the wavelet multi-scale entropy. A support vector machine-based classifier is used for PAF prediction.

Results

The performance of the proposed method in predicting PAF episodes is evaluated with 100 signals from the MIT-BIT PAF prediction database. With regard to the dynamics and uncertainty of PAF signals, our proposed method obtains the values of 92.18, 94.88, and 89.48% for the evaluation criteria of correct rate, sensitivity, and specificity, respectively.

Conclusions

Our proposed method presents better results than the existing studies based on time domain, frequency domain, and non-linear methods. Thus, our method shows considerable potential for clinical monitoring and treatment.

     

Isolation of Fetal ECG Signals from Abdominal ECG Using Wavelet Analysis

ObjectiveMonitoring the heartbeat of the fetus during pregnancy is a vital part in determining their health. Current fetal heart monitoring techniques lack the accuracy in fetal heart rate monitoring and features acquisition, resulting in diagnostic medical issues. The demand for a reliable method of non-invasive fetal heart monitoring is of high importance.MethodElectrocardiogram (ECG) is a method of monitoring the electrical activity produced by the heart. The extraction of the fetal ECG (FECG) from the abdominal ECG (AECG) is challenging since both ECGs of the mother and the baby share similar frequency components, adding to the fact that the signals are corrupted by white noise. This paper presents a method of FECG extraction by eliminating all other signals using AECG. The algorithm is based on attenuating the maternal ECG (MECG) by filtering and wavelet analysis to find the locations of the FECG, and thus isolating them based on their locations. Two signals of AECG collected at different locations on the abdomens are used. The ECG data used contains MECG of a power of five to ten times that of the FECG.ResultsThe FECG signals were successfully isolated from the AECG using the proposed method through which the QRS complex of the heartbeat was conserved, and heart rate was calculated. The fetal heart rate was 135 bpm and the instantaneous heart rate was 131.58 bpm. The heart rate of the mother was at 90 bpm with an instantaneous heart rate of 81.9 bpm.ConclusionThe proposed method is promising for FECG extraction since it relies on filtering and wavelet analysis of two abdominal signals for the algorithm. The method implemented is easily adjusted based on the power levels of signals, giving it great ease of adaptation to changing signals in different biosignals applications.