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.     

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。     

基于小波变换的混合二维ECG数据压缩方法

提出了一种新的基于小波变换的混合二维心电(electrocardiogram,ECG)数据压缩方法。基于ECG数据的两种相关性,该方法首先将一维ECG信号转化为二维信号序列。然后对二维序列进行了小波变换,并利用改进的编码方法对变换后的系数进行了压缩编码:即先根据不同系数子带的各自特点和系数子带之间的相似性,改进了等级树集合分裂(setpartitioninghierarchicaltrees,SPIHT)算法和矢量量化(vectorquantization,VQ)算法;再利用改进后的SPIHT与VQ相混合的算法对小波变换后的系数进行了编码。利用所提算法与已有具有代表性的基于小波变换的压缩算法和其他二维ECG信号的压缩算法,对MIT/BIH数据库中的心律不齐数据进行了对比压缩实验。结果表明:所提算法适用于各种波形特征的ECG信号,并且在保证压缩质量的前提下,可以获得较大的压缩比。     

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。     

多通道脑电信号的盲分离

提出一种新的多通道脑电信号盲分离的方法,将小波变换和独立分量分析(independent component analysis,ICA)相结合,利用小波变换的滤噪作用,将混合在原始脑电的部分高频噪声滤除后,再重构原始脑电作为ICA的输入信号,有效地克服了现有ICA算法不能区分噪声的缺陷。实验结果表明,该方法对多通道脑电的盲分离是很有效的。     

ECG 信号QRS 波群检测算法的进展

QRS波群是ECG信号的重要组成部分,是心电信号分析的基础.QRS波群的检测方法已经有很多种实用有效的方法,并逐步地走向成熟,在实际应用中得到实现.本文就QRS波群的检测方法作了具体的整理与分析,较全面的阐述了实际应用中的各种算法,最后作者对检测算法的发展趋势进行了总结和展望.     

线性时频分析在ECG信号分析中的应用

目的:心电信号(Electrocardio-signal,ECG)是人体中最重要的生物信号之一,是一种具有非平稳性和非线性特性的信号.分析ECG信号是诊断心脏疾病的有利工具,近年来国内外很多学者致力于这方面的研究.本文探讨短时Fourier变换(STFT)和离散小波变换(DWT)这两种时频分析方法在ECG信号分析中的应用.方法:本文采用麻省理工学院的MIT-BIH数据库中提供的数据,运用MATLAB软件编程,讨论短时Fourier变换和离散小波变换在ECG信号分析中的应用.结果:通过编程,做出了正常ECG信号和失常ECG信号的短时Fourier变换的时域图和频谱图以及正常ECG信号和失常ECG信号的单级离散小波变换的结果.结论:正常ECG信号和失常ECG信号的STFT变换的时域图和频谱图都能反应出信号的频率和时间的变化关系.但是,正常信号和失常信号的频率和时间有明显不同,正常信号的能量随时间和频率的变化关系有序整齐,而且周围有较少的杂波;失常信号的能量随时间和频率的变化关系杂乱,而且周围存在较多的杂波.通过离散小波变化后,正常信号和失常信号均产生了不同的离散小波系数,根据不同的离散小波系数,可以很容易判断正常信号和失常信号的区别.     

心音信号的分析方法及其应用

心音信号包含了关于人体丰富的病理信息,其分析与研究为心血管疾病的诊断提供了很重要的临床依据.本文探讨了心音信号分析的研究现状,及其短时傅立叶变换(STFT),小波变换(WT),Hilbert-Huang变换在心音信号分析中的应用以及需要解决的理论问题,并运用PhysioBank数据库中的ECG数据,将Hilbert变换运用于ECG信号的分析中,结合MATLAB软件,做出了较为理想的瞬时频率图.     

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。     

小波变换及滚球算法在单分子荧光能量共振转移图像分析中的应用

单分子荧光共振能量转移技术是通过检测单个分子内的荧光供体及受体间荧光能量转移的效率来研究分子构象的变化．要得到这些生物大分子的信息就需要对大量的单分子信号进行统计分析,人工分析这些信息,既费时费力又不具备客观性和可重复性,因此本文将小波变换及滚球算法应用到单分子荧光能量共振转移图像中对单分子信号进行统计分析．在保证准确检测到单分子信号的前提下,文章对滚球算法和小波变换算法处理图像后的线性进行了分析,结果表明,滚球算法和小波变换算法不但能够很好地去除单分子FRET图像的背景噪声,同时还能很好地保持单分子荧光信号的线性．最后本文还利用滚球算法处理单分子FRET图像及统计15 bp DNA的FRET效率的直方图,通过计算得到了15 bp DNA的FRET效率值．     

A Comparison of ECG Signal Pre-processing Using FrFT,FrWT and IPCA for Improved Analysis

ObjectiveElectrocardiogram (ECG) is a diagnostic tool for recording electrical activities of the human heart non-invasively. It is detected by electrodes placed on the surface of the skin in a conductive medium. In medical applications, ECG is used by cardiologists to observe heart anomalies (cardiovascular diseases) such as abnormal heart rhythms, heart attacks, effects of drug dosage on subject's heart and knowledge of previous heart attacks. Recorded ECG signal is generally corrupted by various types of noise/distortion such as cardiac (isoelectric interval, prolonged depolarization and atrial flutter) or extra cardiac (respiration, changes in electrode position, muscle contraction and power line noise). These factors hide the useful information and alter the signal characteristic due to low Signal-to-Noise Ratio (SNR). In such situations, any failure to judge the ECG signal correctly may result in a delay in the treatment and harm a subject (patient) health. Therefore, appropriate pre-processing technique is necessary to improve SNR to facilitate better treatment to the subject. Effects of different pre-processing techniques on ECG signal analysis (based on R-peaks detection) are compared using various Figures of Merit (FoM) such as sensitivity (Se), accuracy (Acc) and detection error rate (DER) along with SNR.MethodsIn this research article, a new fractional wavelet transform (FrWT) has been proposed as a pre-processing technique in order to overcome the disadvantages of other existing commonly used techniques viz. wavelet transform (WT) and the fractional Fourier transform (FrFT). The proposed FrWT technique possesses the properties of multiresolution analysis and represents signal in the fractional domain which consists of representation in terms of rotation of signals in the time–frequency plane. In the literature, ECG signal analysis has been improvised using statistical pre-processing techniques such as principal component analysis (PCA), and independent component analysis (ICA). However, both PCA and ICA are prone to suffer from slight alterations in either signal or noise, unless the basis functions are prepared with a worldwide set of ECG. Independent Principal Component Analysis (IPCA) has been used to overcome this shortcoming of PCA and ICA. Therefore, in this paper three techniques viz. FrFT, FrWT and IPCA are selected for comparison in pre-processing of ECG signals.ResultsThe selected methods have been evaluated on the basis of SNR, Se, Acc and DER of the detected ECG beats. FrWT yields the best results among all the methods considered in this paper; 34.37dB output SNR, 99.98% Se, 99.96% Acc, and 0.036% DER. These results indicate the quality of biology-related information retained from the pre-processed ECG signals for identifying different heart abnormalities.ConclusionCorrect analysis of the acquired ECG signal is the main challenge for cardiologist due to involvement of various types of noises (high and low frequency). Twenty two real time ECG records have been evaluated based on various FoM such as SNR, Se, Acc and DER for the proposed FrWT and existing FrFT and IPCA preprocessing techniques. Acquired real-time ECG database in normal and disease situations is used for the purpose. The values of FoMs indicate high SNR and better detection of R-peaks in a ECG signal which is important for the diagnosis of cardiovascular disease. The proposed FrWT outperforms all other techniques and holds both analytical attributes of the actual ECG signal and alterations in the amplitudes of various ECG waveforms adequately. It also provides signal portrayals in the time-fractional-frequency plane with low computational complexity enabling their use practically for versatile applications.     

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.     

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.     

Continuous EEG signal analysis for asynchronous BCI application

In this study, we propose a two-stage recognition system for continuous analysis of electroencephalogram (EEG) signals. An independent component analysis (ICA) and correlation coefficient are used to automatically eliminate the electrooculography (EOG) artifacts. Based on the continuous wavelet transform (CWT) and Student's two-sample t-statistics, active segment selection then detects the location of active segment in the time-frequency domain. Next, multiresolution fractal feature vectors (MFFVs) are extracted with the proposed modified fractal dimension from wavelet data. Finally, the support vector machine (SVM) is adopted for the robust classification of MFFVs. The EEG signals are continuously analyzed in 1-s segments, and every 0.5 second moves forward to simulate asynchronous BCI works in the two-stage recognition architecture. The segment is first recognized as lifted or not in the first stage, and then is classified as left or right finger lifting at stage two if the segment is recognized as lifting in the first stage. Several statistical analyses are used to evaluate the performance of the proposed system. The results indicate that it is a promising system in the applications of asynchronous BCI work.     

A Compound Fault Diagnosis for Rolling Bearings Method Based on Blind Source Separation and Ensemble Empirical Mode Decomposition

A Compound fault signal usually contains multiple characteristic signals and strong confusion noise, which makes it difficult to separate week fault signals from them through conventional ways, such as FFT-based envelope detection, wavelet transform or empirical mode decomposition individually. In order to improve the compound faults diagnose of rolling bearings via signals’ separation, the present paper proposes a new method to identify compound faults from measured mixed-signals, which is based on ensemble empirical mode decomposition (EEMD) method and independent component analysis (ICA) technique. With the approach, a vibration signal is firstly decomposed into intrinsic mode functions (IMF) by EEMD method to obtain multichannel signals. Then, according to a cross correlation criterion, the corresponding IMF is selected as the input matrix of ICA. Finally, the compound faults can be separated effectively by executing ICA method, which makes the fault features more easily extracted and more clearly identified. Experimental results validate the effectiveness of the proposed method in compound fault separating, which works not only for the outer race defect, but also for the rollers defect and the unbalance fault of the experimental system.     

Removing ECG contamination from EMG recordings: a comparison of ICA-based and other filtering procedures

Trunk muscle electromyography (EMG) is often contaminated by the electrocardiogram (ECG), which hampers data analysis and potentially yields misinterpretations. We propose the use of independent component analysis (ICA) for removing ECG contamination and compared it with other procedures previously developed to decontaminate EMG. To mimic realistic contamination while having uncontaminated reference signals, we employed EMG recordings from peripheral muscles with different activation patterns and superimposed distinct ECG signals that were recorded during rest at conventional locations for trunk muscle EMG. ICA decomposition was performed with and without a separately collected ECG signal as part of the data set and contaminated ICA modes representing ECG were identified automatically. Root mean squared relative errors and correlations between the linear envelopes of uncontaminated and contaminated EMG were calculated to assess filtering effects on EMG amplitude. Changes in spectral content were quantified via mean power frequencies. ICA-based filtering largely preserved the EMG's spectral content. Performance on amplitude measures was especially successful when a separate ECG recording was included. That is, the ICA-based filtering can produce excellent results when EMG and ECG are indeed statistically independent and when mode selection is flexibly adjusted to the data set under study.     

K-means algorithm for the detection and delineation of QRS-complexes in Electrocardiogram

Electrocardiogram (ECG) is an important bioelectrical signal used to asses the cardiac state of a patient. It consists of a recurrent wave sequence of P-wave, QRS-complex and T-wave associated with each beat. The QRS-complex is the prominent feature of the ECG. This paper presents a simple method using K-means clustering algorithm for the detection of QRS-complexes in ECG signal. Digital filters are used to remove the power line interference and baseline wander present in the ECG signal. K-means algorithm is used to classify QRS and non-QRS-region in the ECG signal. The performance of the algorithm is validated using dataset-3 of the CSE multi-lead measurement library. Detection rate of 98.66% is obtained. The percentage of false positive and false negative is 1.14% and 1.34% respectively. The mean and standard deviation of the errors between automatic and manual annotations is calculated to validate the delineation performance of the algorithm. The onsets and offsets of the detected QRS-complexes are found well within the tolerance limits as specified by the CSE library.     

Revisiting QRS Detection Methodologies for Portable,Wearable, Battery-Operated,and Wireless ECG Systems

Cardiovascular diseases are the number one cause of death worldwide. Currently, portable battery-operated systems such as mobile phones with wireless ECG sensors have the potential to be used in continuous cardiac function assessment that can be easily integrated into daily life. These portable point-of-care diagnostic systems can therefore help unveil and treat cardiovascular diseases. The basis for ECG analysis is a robust detection of the prominent QRS complex, as well as other ECG signal characteristics. However, it is not clear from the literature which ECG analysis algorithms are suited for an implementation on a mobile device. We investigate current QRS detection algorithms based on three assessment criteria: 1) robustness to noise, 2) parameter choice, and 3) numerical efficiency, in order to target a universal fast-robust detector. Furthermore, existing QRS detection algorithms may provide an acceptable solution only on small segments of ECG signals, within a certain amplitude range, or amid particular types of arrhythmia and/or noise. These issues are discussed in the context of a comparison with the most conventional algorithms, followed by future recommendations for developing reliable QRS detection schemes suitable for implementation on battery-operated mobile devices.