12导联同步心电图异常波形数据库生成系统与波形分析初步

建立了 12导联同步心电异常波形数据库生成系统 ,并在此基础上研究了 12导联心电图实时分析与基于小波变换的QRS波自动识别算法。本研究可为临床医疗、教学和科研及心电自动分析软件和仪器的研制奠定基础 ,便于与国际心电数据库接轨。     

Design of a wavelet interpolation filter for enhancement of the ST-segment

A wavelet interpolation filter (WIF) is designed for the removal of motion artifacts in the ST-segment of stress ECGs. The WIF consists of two parts. One part is a wavelet transform that decomposes the stress ECG signal into several frequency bands using a Haar wavelet. The other part is an interpolation method, such as the spline technique, that is used to enhance the reconstruction performance of the signal decomposed by the wavelet transform. To evaluate the performance of the WIF, three indices are used: signal-to-noise ratio (SNR), reconstruction square error (RSE) and standard deviation (SD). The MIT/BIH arrhythmia database, the European ST-T database and the triangular wave are used for evaluation. A noisy ECG signal, corrupted by motion artifacts, is simulated by the addition of two types of random noise to the original ECG signal. For comparison, three indices for the other methods are also computed: mean, median and hard thresholding. The performance of the WIF shows that RSE, SNR and SD are 392.7, 18.3 dB and 2.6, respectively, in the case of a noisy signal with an SNR of 7.1 dB. This result is much better than those for the other methods.     

Application of a wavelet adaptive filter to minimise distortion of the ST-segment

A wavelet adaptive filter (WAF) for the removal of baseline wandering in ECG signals is described. The WAF consists of two parts. The first part is a wavelet transform that decomposes the ECG signal into seven frequency bands using Vaidyanathan-Hoang wavelets. The second part is an adaptive filter that uses the signal of the seventh lowest-frequency band among the wavelet transformed signals as primary input and a constant as reference input. To evaluate the performance of the WAF, two baseline wandering elimination filters are used, a commercial standard filter with a cutoff frequency of 0.5 Hz and a general adaptive filter. The MIT/BIH database and the European ST-T database are used for the evaluation. The WAF performs better in the average power of eliminated noise than the standard filter and adaptive filter. Furthermore, it shows a lower ST-segment distortion than the standard filter and the adaptive filter.     

ECG信号小波变换与峰谷检测算法的研究

本文在ECG信号检测过程中，将ECG信号在3尺度上的Haar小波分解的细节信号模极大值对检测与数学形态学峰谷检测相结合，提出了ECG信号小波变换与峰谷检测算法，该算法弥补了小波变换算法对ECG信号时域特征检测的不足，有效地提高了ECG信号检测的准确度。     

基于小波变换与形态学运算的ECG综合检测算法的研究

针对心电波形检测中小波变换算法的缺点 ,在 ECG特征点检测中 ,将原始信号在 3尺度上的 haar小波分解的细节信号模极大值对检测法与数学形态学峰谷检测相结合 ,提出了一种新的心电波形特征点综合检测算法 ,该算法弥补了小波变换算法对信号振幅检测上的不足 ,有效地提高了心电信号特征点检测的准确度。     

心电信号QRS波的识别算法及程序设计

实现心电图QRS波检测的算法有很多,本文介绍了一种算法,即利用波变换的多尺特性,可以将QRS波从高P波,高T波,噪声,基线漂移和伪迹中分离出灵,并采用Microsoft VisualC 5.0编程实现算法,使用该方法对MIT／BIH心电数据库中带有严重基线漂移和噪声的心电信号进行处理,对QRS的识别率高达99．8％,文中给出给程序设计要点和程序流程图。     

ECG信号的小波变换检测方法

本文反小波变换应用于ＥＣＧ信号的ＱＲＳ波检测。利用二进样条小波对信号按Ｍａｌｌａｔ算法进行变换：从二进小波变换的等效滤波器的角度，分析了信号奇异点（Ｒ峰点）与其小波变换模极大值对的零交叉点的关系。在检测中运用了一系列策略以增强算法的抗干扰能力、提高ＱＲＳ波的正确检测率。经ＭＩＴ／ＢＩＨ标准心电数据库检测验证，ＱＲＳ波正确检测率高达９９．８％。     

Detection of life-threatening cardiac arrhythmias using the wavelet transformation

Time-frequency wavelet theory is used for the detection of life threatening electrocardiography (ECG) arrhythmias. This is achieved through the use of the raised cosine wavelet transform (RCWT). The RCWT is found to be useful in differentiating between ventricular fibrillation, ventricular tachycardia and atrial fibrillation. Ventricular fibrillation is characterised by continuous bands in the range of 2–10 Hz; ventricular tachycardia is characterised by two distinct bands: the first band in the range of 2–5 Hz and the second in the range of 6–8 Hz; and atrial fibrillation is determined by a low frequency band in the range of 0–5 Hz. A classification algorithm is developed to classify ECG records on the basis of the computation of three parameters defined in the time-frequency plane of the wavelet transform. Furthermore, the advantage of localising and separating ECG signals from high as well as intermediate frequencies is demonstrated. The above capabilities of the wavelet technique are supported by results obtained from ECG signals obtained from normal and abnormal subjects.     

基于小波变换的心律失常判别算法

本文介绍一种基于小波变换的心律失常判别算法。该算法利用连续小波变换及其在不同尺度上的变化规律对心电信号进行分析 ,可以对常见的六种心律失常进行自动判别。通过采用MIT心电数据库的数据进行测试 ,QRS波的正确检出率在 99%以上 ,而室性期前收缩、房性期前收缩的正确检出率在 90 %以上。     

Suppression of electromyogram interference on the electrocardiogram by transform domain denoising

A method for suppression of electromyogram (EMG) interference in electrocardiogram (ECG) recordings is presented. By assuming that the EMG is long-term non-stationary Gaussian noise, two successive decompositions were proposed, and the data transformed for Wiener filtering. Successive ECG cycles were rearranged and aligned by the R-wave, forming a matrix containing separated heart cycles in its rows. A short-window discrete cosine transform (DCT) was applied to the columns of the matrix for inter-cycle de-correlation. Next, Weiner filtering in a translation-invariant wavelet domain was performed on the DCT-transformed matrix rows for de-correlation of the data into each ECG cycle. The method resulted in an improvement in the signal-to-noise ratio of more than 10 db, a threefold reduction in mean relative amplitude errors and reduced ripple artifacts around the signal transients, thus preserving the waveform in diagnostically important signal segments.     

Elimination of electrocardiogram artifacts from electroencephalogram records by using the simultaneously recorded electrocardiograph data

We developed a simple method to eliminate electrocardiogram (ECG) artifacts from electroencephalogram (EEG) records by using simultaneously recorded ECG data. The raw EEG data, the real EEG data and the ECG data were regarded as multi-dimensional vectors Ea, Er and C, respectively. Also, the ECG data, with reduced amplitude whose coefficient was denoted as 'k', were assumed to be overlapped on the real EEG. These assumptions introduced the equations [Ea = Er + k.C], [Er.C = 0] and finally [k = Ea. C/C.C]. This calculation method was implemented by a Macintosh computer using data exported from digital EEG recordings (sampled at 200 Hz with 16-bit resolution). In several subjects, sampling intervals of 5 or 10 seconds for calculation succeeded in eliminating ECG artifacts. However, regardless of the sampling interval, this elimination condition was not always efficient in several other subjects, including a brain-dead patient. It was suggested that the ECG data used were insufficient for the calculation, because only one hand-to-hand reference was used for simultaneous recording, as usual. This one ECG reference was able to express only one ECG projection. Then two other hand-to-foot references of ECG were added to the recordings, and the elimination procedure was performed using all of the simultaneously recorded ECG data at the three references. Consequently, elimination was much improved in most subjects, including the brain-dead patient. Our method may be useful for eliminating ECG artifacts without changing reference electrodes.     

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.     

小波分析在心电信号处理中的应用

主要从三个方面(心电信号的预处理、参数检测和波形检测以及心电图的压缩)综述了小波分析在心电信号处理中的应用，对各种算法进行了比较和评价，并对目前所存在的问题进行了初步探讨。     

ECG analysis using wavelet transform: application to myocardial ischemia detection

In this paper, we propose a method for the detection of myocardial ischemic events from electrocardiogram (ECG) signal using the wavelet transform technique. The wavelet transform is obtained using the quadratic spline wavelet. Then, based on the wavelet transform values, the characteristic points of the ECG signal are found out. These characteristic points are used to identify any ischemic episodes present in the ECG signal. This technique can be extended for other types of cardiac abnormality detections, which induce changes in the ECG.     

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。     

高可靠度心率变异性信号获取的实验研究

心率变异性信号的获取在生理研究和临床诊断中都有着重要的应用价值。为了保证心率变异性分析的准确性，必须考虑心率变异性的获取方法。本文利用信号奇异点及其小波变换的关系，设计了HRV信号的R波获取软件。对MIT／BIH心电数据库中的37个记录文件进行R波的检测实验，检测实验效果令人满意。     

基于小波包理论的ECG信号数据压缩

小波包函数族是小波函数的推广，与一般的小波函数相比小波包函数具有更好时频局域特性。本文在小波包理论的基础上对医学上常用的心电图信号（ＥＣＧ）进行了数据压缩实验。实验结果表明，在不影响诊断分析的条件下，小波包压缩技术可以达到令人满意的效果。     

SADE3: an effective system for automated detection of epileptiform events in long-term EEG based on context information

Interictal spike detection is a time-consuming, low-efficiency task, but is important to epilepsy diagnosis. Automated systems reported to date usually have their practical efficacy compromised by elevated rates of false-positive detections per minute, which are caused mainly by the influence of artifacts (such as noise activity and ocular movements) and by the adoption of single or simple approaches. This work describes the development of a hybrid system for automatic detection of spikes in long-term electroencephalogram (EEG), named System for Automatic Detection of Epileptiform Events in EEG (SADE(3)), which uses wavelet transform, neural networks and artificial intelligence procedures to recognize epileptic and to reject non-epileptic activity. The system's pre-processing stage filters the EEG epochs with the Coiflet wavelet function, which showed the closest correlation to epileptogenic (EPG) activity, in opposition to some other wavelet functions that did not correlate with these events. In contrast to current attempts using continuous wavelet transform, we chose to work with fast wavelet transform to reduce processing time and data volume. Detail components at appropriate decomposition levels were used to accentuate spikes, sharp waves, high-frequency noise activity and ocular artifacts. These four detailed components were used to train four specialized neural networks, designed to detect and classify the EPG and non-EPG events. An expert module analyzes the networks' outputs, together with multichannel and context information and concludes the detection. The system was evaluated with 126,000 EEG epochs, obtained from seven different patients during long-term monitoring, under diverse behavior and mental states. More than 6,721 spikes and sharp waves were previously identified by three experienced human electroencephalographers. In these tests, the SADE(3) system simultaneously achieved 70.9% sensitivity, 99.9% specificity and a rate of 0.13 false-positives per minute, indicating its usefulness and low vulnerability to artifact influence. After tests, the SADE(3) system showed itself to be able to process bipolar cortical EEG records, from long-term monitoring, up to 32 channels, without any data preparation or event positioning. At the same time, SADE(3) revealed a high capacity to reject non-epileptic paroxysms, robustness in relation to a variety of spike morphologies, flexibility in adjustment of performance rates and the capacity to actually save time during EEG reading. Furthermore, it can be adapted to other applications for pattern recognition, with simple adjustments.     

Optimal wavelets for biomedical signal compression

Signal compression is gaining importance in biomedical engineering due to the potential applications in telemedicine. In this work, we propose a novel scheme of signal compression based on signal-dependent wavelets. To adapt the mother wavelet to the signal for the purpose of compression, it is necessary to define (1) a family of wavelets that depend on a set of parameters and (2) a quality criterion for wavelet selection (i.e., wavelet parameter optimization). We propose the use of an unconstrained parameterization of the wavelet for wavelet optimization. A natural performance criterion for compression is the minimization of the signal distortion rate given the desired compression rate. For coding the wavelet coefficients, we adopted the embedded zerotree wavelet coding algorithm, although any coding scheme may be used with the proposed wavelet optimization. As a representative example of application, the coding/encoding scheme was applied to surface electromyographic signals recorded from ten subjects. The distortion rate strongly depended on the mother wavelet (for example, for 50% compression rate, optimal wavelet, mean±SD, 5.46±1.01%; worst wavelet 12.76±2.73%). Thus, optimization significantly improved performance with respect to previous approaches based on classic wavelets. The algorithm can be applied to any signal type since the optimal wavelet is selected on a signal-by-signal basis. Examples of application to ECG and EEG signals are also reported.     

Separation of electrocardiographic and encephalographic components based on signal averaging and wavelet shrinkage techniques

During ambulatory monitoring, it is often required to record the electroencephalogram (EEG) and the electrocardiogram (ECG) simultaneously. It would be ideal if both EEG and ECG can be obtained with one measurement. We introduce an algorithm combining the wavelet shrinkage and signal averaging techniques to extract the EEG and ECG components from an EEG lead signal to a noncephalic reference (NCR). The evaluation using simulation data and measured data showed that the normalized power spectrum unvaried in all frequency bands for the EEG components, and the sensitivity and specificity of R-wave detection for the ECG component were nearly 100%.