边缘学科分支最新动向

丁N一05 1 96041402小波变换在心电图QRS波检测中的应用/李智,黄智(北京工业大学)11北京生物医学工程一1996,(1).一10~13 文章介绍一种利用信号的小波变换在多尺度边沿上的综合特性,提出了一种新的QRs波检测法.采用Manat快速算法获得原始EcG信号在不同尺度上的小波分解信号,将含有大部分高频QRS波在多尺度上的分解信号送人一个线性自适应匹配滤波器、匹配滤波器的输出用于检测R波的位置.对MIT数据库中的数据进行了检测,R波的检测率可达99.8%.图3表l参6(许)15(1)一19~22 文中介绍了zEP系列诱发电位/肌电图仪体感刺激器的工作原理.该…     

基于非下采样轮廓小波变换的图像去噪

针对采用下采样滤波器结构的轮廓波、轮廓小波在图像去噪过程中会引入伪吉布斯现象,利用小波变换(WT)和非下采样方向滤波器组(NDFB)构造了一种新的多尺度、多分辨率图像的非下采样轮廓小波变换(NWCT)。WT去除了拉普拉斯金字塔滤波器(LPF)的计算冗余,NDFB保证了该变换具有平移不变性。为了验证该变换的有效性,对其进行了图像去噪实验。实验结果表明,所提出方法能获得比WT、轮廓波变换(CT)、轮廓小波变换(WCT)更高的峰值信噪比(PSNR),并且能够很好地抑制伪吉布斯现象。     

基于小波变换的QRS波群检测

提出了一种基于小波多分辨分析的算法,对心电信号进行特征提取和识别。通过小波变换对常规心电图信号进行分解去噪和特征提取,并利用动态自适应阈值和删除多检点,补偿漏检点对QRS波检测进行优化。实验结果表明该方法在QRS波形不失真的情况下,提高了一部分MIT-BIH数据库信号中QRS波识别的准确率,并且对于较低准确率的心电信号的原因进行了分析。     

基于小波变换的QRS波检测算法

针对基于小渡变换的心电信号QRS波的检测算法的计算量较大,硬件不易实现的问题,提出一种FPGA的实现方案.首先分析了利用小波变换检测QRS波群的算法,给出硬件实现方案,该算法由小波变换模块和检测模块两个模块实现.然后选取高端FPGA作为硬件处理平台,给出小波变换模块及波形检测模块具体实现结构.最后在Quartus Ⅱ下进行编译和仿真,完成心电信号检测算法的硬件实现.从综合后的资源占有率上可以看出系统充分利用了FPGA内部丰富的资源,从仿真的结果看出在FPGA系统上准确的检测出了QRS波.     

基于小波变换的自适应QRS-T对消P波检测算法

该文提出一种基于小波变换的自适应QRS-T对消P波检测算法。首先采用二进Marr小波的Mallat算法对心电信号作多尺度分解,在每个尺度下只保留超过一定阈值的小波模极大值点,其它点置零处理。在小波分解的3,4尺度下检测QRS波群,并根据心拍节律信息和QT间期,将QRS-T波群所对应的小波模极大值点进行自适应对消,最后对包含P波的剩余信号进行非线性放大,利用小波模极大值的自适应阈值检测方法定位P波。该方法经MIT-BIH心电数据库数据验证,取得了满意的结果。     

不同小波基函数下的语音去噪研究

为了分析语音去噪的效果,首先介绍了小波变换和分解的相关理论知识,然后对Daubechies小波、Symmlets小波、Coiflets小波和Haar小波特性做了比较分析。最后选取一段添加了高斯白噪声的实际语音信号,选取heursure启发式阈值,利用Matlab软件分别对各种小波基下的去噪效果进行仿真实验。并通过计算去噪前后的信噪比(SNR)和最小均方差(MSE)的值,分析比较各种小波基函数的去噪效果,并得出最优小波基函数。     

基于小波包的雷达波形设计与性能分析

研究了如何利用小波包的相关理论来设计雷达波形,从小波和小波包的基本理论入手,通过分析已有的小波包的特性,提出了级联小波包来构造雷达波形的设计方法。使用自相关函数、模糊函数和时频分布等分析工具,对设计的雷达波形进行了性能分析。理论分析和计算机仿真结果均表明,基于小波包设计的雷达波形与传统的线性调频信号(LFM)相比具有更低的压缩旁瓣和更复杂的脉内特征。文中的研究成果能够为雷达的波形设计提供一种可行的方法和思路。     

基于小波和扩展卡尔曼的心电检测

文中将小波变换和扩展卡尔曼滤波器相结合,利用小波变换多尺度多分辨的特点,将心电信号进行分解。然后对心电信号在各尺度上进行扩展卡尔曼滤波。最后在扩展卡尔曼滤波的输出结果上进行QRS波形检测。文中方法经MIT-BIH心电数据库检验,QRS波Se（探测灵敏度）在99.40%以上,同时,QRS＋P（正探测率）在99.39%以上,提高了心电信号检测的正确率。     

时频分布的分辨率比较

利用时频聚集性较好的高斯函数作为标准信号,分析了短时傅里叶变换、小波变换、谱图、尺度图、Wigner-Ville分布的分辨率以及交叉项对分辨率的影响,并对它们的分辨率进行了比较;为了进行时间-尺度分布与时频分布的比较,提出了改进小波变换;在分辨率比较中,给出了几种典型情况的模拟结果.     

基于发动机冷试的非平稳信号分析与特征提取

联合时频是分析非平稳信号的有力工具,文章通过几种时频分析如STFT、Wigner-Ville分布、小波变换和小波能量商,对发动机冷试振动信号进行分析。结果表明:STFT、WV、及WT均能一定程度反应信号的时频分布,STFT频率分辨率有限,WV分布存在一定的交叉干扰项,小波变换能够在各个尺度对信号进行观察,小波包能量商能够清楚地观察信号的能量分布,能够作为发动机状态的特征向量。     

Wavelet transform-based QRS complex detector

In this paper, we describe a QRS complex detector based on the dyadic wavelet transform (Dy WT) which is robust to time-varying QRS complex morphology and to noise. We design a spline wavelet that is suitable for QRS detection. The scales of this wavelet are chosen based on the spectral characteristics of the electrocardiogram (ECG) signal. We illustrate the performance of the Dy WT-based QRS detector by considering problematic ECG signals from the American Heart Association (AHA) data base. Seventy hours of data was considered. We also compare the performance of Dy WT-based QRS detector with detectors based on Okada, Hamilton-Tompkins, and multiplication of the backward difference algorithms. From the comparison, results we observed that although no one algorithm exhibited superior performance in all situations, the Dy WT-based detector compared well with the standard techniques. For multiform premature ventricular contractions, bigeminy, and couplets tapes, the Dy WT-based detector exhibited excellent performance.     

Using wavelet transform and fuzzy neural network for VPC detection from the Holter ECG

A novel method for detecting ventricular premature contraction (VPC) from the Holter system is proposed using wavelet transform (WT) and fuzzy neural network (FNN). The basic ideal and major advantage of this method is to reuse information that is used during QRS detection, a necessary step for most ECG classification algorithm, for VPC detection. To reduce the influence of different artifacts, the filter bank property of quadratic spline WT is explored. The QRS duration in scale three and the area under the QRS complex in scale four are selected as the characteristic features. It is found that the R wave amplitude has a marked influence on the computation of proposed characteristic features. Thus, it is necessary to normalize these features. This normalization process can reduce the effect of alternating R wave amplitude and achieve reliable VPC detection. After normalization and excluding the left bundle branch block beats, the accuracies for VPC classification using FNN is 99.79%. Features that are extracted using quadratic spline wavelet were used successfully by previous investigators for QRS detection. In this study, using the same wavelet, it is demonstrated that the proposed feature extraction method from different WT scales can effectively eliminate the influence of high and low-frequency noise and achieve reliable VPC classification. The two primary advantages of using same wavelet for QRS detection and VPC classification are less computation and less complexity during actual implementation.     

New Concepts for PVC Detection

A new scheme is proposed for the detection of premature ventricular beats, which is a vital function in rhythm monitoring of cardiac patients. A transformation based on the first difference of the digitized electrocardiogram (ECG) signal is developed for the detection and delineation of QRS complexes. The method for classifying the abnormal complexes from the normal ones is based on the concepts of minimum phase and signal length. The parameters of a linear discriminant function obtained from a training feature vector set are used to classify the complexes. Results of application of the scheme to ECG of two arrhythmia patients are presented.     

Ventricular late potentials characterization in time-frequencydomain by means of a wavelet transform

The main transforms of Cohen's class allow signal representation simultaneously in time and frequency domains. Wavelet transforms make it possible to link the temporal window width to the analyzing frequency and leads to a “modified wavelet transform” which improves resolution both in time and frequency. A simulation study illustrates the artifacts of every time-frequency representation on pure sinusoids and gives performance evaluation of the different methods when searching a sinusoid embedded in a QRS complex. Analyses of real signals from healthy and pathological subjects confirm the simulation results and complete the characterization of ventricular late potentials yet detected by signal averaging     

Classification of cardiac arrhythmias using fuzzy ARTMAP

The authors have investigated the QRS complex, extracted from electrocardiogram (EGG) data, using fuzzy adaptive resonance theory mapping (ARTMAP) to classify cardiac arrhythmias. Two different conditions have been analyzed: normal and abnormal premature ventricular contraction (PVC). Based on MIT/BIH database annotations, cardiac beats for normal and abnormal QRS complexes were extracted from this database, scaled, and Hamming windowed, after bandpass filtering, to yield a sequence of 100 samples for each QRS segment. From each of these sequences, two linear predictive coding (LPC) coefficients were generated using Burg's maximum entropy method. The two LPC coefficients, along with the mean-square value of the QRS complex segment, were utilized as features for each condition to train and test a fuzzy ARTMAP neural network for classification of normal and abnormal PVC conditions. The test results show that the fuzzy ARTMAP neural network can classify cardiac arrhythmias with greater than 99% specificity and 97% sensitivity     

Estimation of QRS complex power spectra for design of a QRS filter

We present power spectral analysis of ECG waveforms as well as isolated QRS complexes and episodes of noise and artifact. The power spectral analysis shows that the QRS complex could be separated from other interfering signals. A bandpass filter that maximizes the signal (QRS complex)-to-noise (T-waves, 60 Hz, EMG, etc.) ratio would be of use in many ECG monitoring instruments. We calculate the coherence function and, from that, the signal-to-noise ratio. Upon carrying out this analysis on experimentaly obtained ECG data, we observe that a bandpass filter with a center frequency of 17 Hz and a Q of 5 yields the best signal-to-noise ratio.     

Real-time discrimination of ventricular tachyarrhythmia withFourier-transform neural network

The authors have developed a method to discriminate life-threatening ventricular arrhythmias by observing the QRS complex of the electrocardiogram (ECG) in each heartbeat. Changes in QRS complexes due to rhythm origination and conduction path were observed with the Fourier transform, and three kinds of rhythms were discriminated by a neural network. In this paper, the potential of the authors' method for clinical uses and real-time detection was examined using human surface ECGs and intracardiac electrograms (EGMs). The method achieved high sensitivity and specificity (>0.98) in discrimination of supraventricular rhythms from ventricular ones. The authors also present a hardware implementation of the algorithm on a commercial single-chip CPU     

Detection of Cardiac Signal Characteristic Point Using Log-Domain Wavelet Transform Circuits

To meet the requirement of low power consumption in biomedical implantable pacemaker applications, a novel method based on balanced log-domain wavelet transform (WT) circuits has been developed for detecting QRS complexes of cardiac signals. By using a hybrid particle swarm optimization algorithm (PSO) combined with sequential quadratic programming, an excellent approximation of the first derivative of a Gaussian wavelet is achieved. The WT circuits are composed of filters whose impulse response is the approximation of the Gaussian wavelet. The WT filter design is based on a time inverse follow-the-leader feedback structure with class-AB balanced log-domain integrators as the main building blocks. HSPICE simulation shows that the power consumption is only 62 nW per scale for a 1.2 V supply, and the dynamic range is 86 dB for 2% total harmonic distortion. The high accuracy of the QRS complex detection method has been validated using the MIT-BIH database. This work was supported by the National Natural Science Foundation of China under Grant No. 50677014, Doctoral Special Fund of Ministry of Education under Grant No. 20060532002, High-Tech Research and Development Program of China (No. 2006AA04A104), the Program for New Century Excellent Talents in University of China (NCET-04-0767), and the Foundation of Hunan Provincial Science and Technology (06JJ2024).     

Detection of ECG characteristic points using wavelet transforms

An algorithm based on wavelet transforms (WT's) has been developed for detecting ECG characteristic points. With the multiscale feature of WT's, the QRS complex can be distinguished from high P or T waves, noise, baseline drift, and artifacts. The relation between the characteristic points of ECG signal and those of modulus maximum pairs of its WT's is illustrated. By using this method, the detection rate of QRS complexes is above 99.8% for the MIT/BIH database and the P and T waves can also be detected, even with serious base line drift and noise     

Beat-to-Beat ECG ventricular late potentials variance detection by filter bank and wavelet transform as beat-sequence filter

This paper presents a novel method that employs a wavelet transform and filter bank to detect ventricular late potentials (VLPs) from beat to beat in order to keep its variance. Conventionally, three time-domain features, which are highly related to the QRS complex endpoint, are generally accepted as criteria for classifying VLPs. Signal averaging is a general and effective de-noising method in electroencephalogram late potentials detection, but it may also eliminate the beat-to-beat variance. Other types of filter applied to the time sequence may destroy the late potentials as well when trying to filter out the noise. To preserve the variance from beat to beat as well as late potentials as much as possible, the concept of a beat-sequence filter will be introduced and the wavelet transform can be directly applied to the beat sequence, as will be demonstrated in this paper. After de-noising, instead of applying the voltage comparison on the de-noised signal to determine the QRS complex endpoint, the signal will be processed by a filter bank, and the QRS complex endpoint will be determined by consideration of the correlation between two beats. Both simulation and clinical experimental results will be presented to illustrate the effectiveness of this method.