A hybrid ECG compression algorithm based on singular value decomposition and discrete wavelet transform

Increasing use of computerized ECG processing systems requires effective electrocardiogram (ECG) data compression techniques which aim to enlarge storage capacity and improve data transmission over phone and internet lines. This paper presents a compression technique for ECG signals using the singular value decomposition (SVD) combined with discrete wavelet transform (DWT). The central idea is to transform the ECG signal to a rectangular matrix, compute the SVD, and then discard small singular values of the matrix. The resulting compressed matrix is wavelet transformed, thresholded and coded to increase the compression ratio. The number of singular values and the threshold level adopted are based on the percentage root mean square difference (PRD) and the compression ratio required. The technique has been tested on ECG signals obtained from MIT-BIH arrhythmia database. The results showed that data reduction with high signal fidelity can thus be achieved with average data compression ratio of 25.2:1 and average PRD of 3.14. Comparison between the obtained results and recently published results show that the proposed technique gives better performance.     

High efficient ECG compression based on reversible round-off non-recursive 1-D discrete periodized wavelet transform

Error propagation and word-length-growth are two intrinsic effects influencing the performance of wavelet-based ECG data compression methods. To overcome these influences, a non-recursive 1-D discrete periodized wavelet transform (1-D NRDPWT) and a reversible round-off linear transformation (RROLT) theorem are developed. The 1-D NRDPWT can resist truncation error propagation in decomposition processes. By suppressing the word- length-growth effect, RROLT theorem enables the 1-D NRDPWT process to obtain reversible octave coefficients with minimum dynamic range (MDR). A non-linear quantization algorithm with high compression ratio (CR) is also developed. This algorithm supplies high and low octave coefficients with small and large decimal quantization scales, respectively. Evaluation is based on the percentage root-mean-square difference (PRD) performance measure, the maximum amplitude error (MAE), and visual inspection of the reconstructed signals. By using the MIT-BIH arrhythmia database, the experimental results show that this new approach can obtain a superior compression performance, particularly in high CR situations.     

ECG beat classification based on discrete wavelet transformation and nearest neighbour classifier

Abstract

Myocardial infarction (MI) is a coronary artery disease acquired due to the lack of blood supply in one or more sections of the myocardium, resulting in necrosis in that region. It has different types based on the region of necrosis. In this paper, a statistical approach for classification of Anteroseptal MI (ASMI) is proposed. The first step of the method involves noise elimination and feature extraction from the Electrocardiogram (ECG) signals, using multi-resolution wavelet analysis and thresholding-based techniques. In the next step a classification scheme is developed using the nearest neighbour classification rule (NN rule). Both temporal and amplitude features relevant for automatic ASMI diagnosis are extracted from four chest leads v1–v4. The distance metric for NN classifier is calculated using both Euclidian distance and Mahalanobis distance. A relative comparison between these two techniques reveals that the later is superior to the former, as evident from the classification accuracy. The proposed method is tested and validated using the PTB diagnostic database. Classification accuracy for Mahalanobis distance and Euclidean distance-based NN rule are 95.14% and 81.83%, respectively.     

A novel method for analysis of single ion channel signal based on wavelet transform

A single ion channel signal was analysed by the power distribution fraction constructed by a discrete wavelet transform. Average opening time and energy distribution of the signal can be obtained directly by this method. The method can also be used when the signal is corrupted by noise. By contrast, the conventional frequency domain analysis method--power spectral density--is less effective. Power distribution fraction will therefore give more useful information in analysis of experimental ion channel signals, principally by giving values of the mean channel opening time. The method may be applied to distinguish different ion channels more efficiently and to find their reactions to drugs.     

Analysis of spike-wave discharges in rats using discrete wavelet transform

A feature is a distinctive or characteristic measurement, transform, structural component extracted from a segment of a pattern. Features are used to represent patterns with the goal of minimizing the loss of important information. The discrete wavelet transform (DWT) as a feature extraction method was used in representing the spike-wave discharges (SWDs) records of Wistar Albino Glaxo/Rijswijk (WAG/Rij) rats. The SWD records of WAG/Rij rats were decomposed into time-frequency representations using the DWT and the statistical features were calculated to depict their distribution. The obtained wavelet coefficients were used to identify characteristics of the signal that were not apparent from the original time domain signal. The present study demonstrates that the wavelet coefficients are useful in determining the dynamics in the time-frequency domain of SWD records.     

Electrocardiogram signals de-noising using lifting-based discrete wavelet transform

This paper introduces an effective technique for the denoising of electrocardiogram (ECG) signals corrupted by nonstationary noises. The technique is based on a second generation wavelet transform and level-dependent threshold estimator. Here, wavelet coefficients of ECG signals were obtained with lifting-based wavelet filters. A lifting scheme is used to construct second-generation wavelets and is an alternative and faster algorithm for a classical wavelet transform. The overall denoising performance of our proposed method is considered in relation to several measuring parameters, including types of wavelet filters (Haar, Daubechies 4 (DB4), Daubechies 6 (DB6), Filter(9-7), and Cubic B-splines), thresholding method, and decomposition depth. Three different kinds of noise were considered in this work: muscle artifact noise, electrode motion artifact noise, and white noise. Global performance is evaluated by means of the signal-to-noise ratio and visual inspection. Numerical results comparing the performance of the proposed method with that of nonlinear filtering techniques (median filter) are given. The results demonstrate consistently superior denoising performance of the proposed method over median filtering.     

Performance improvement of the SPIHT coder based on statistics of medical ultrasound images in the wavelet domain

This paper proposes some modifications to the state-of-the-art Set Partitioning In Hierarchical Trees (SPIHT) image coder based on statistical analysis of the wavelet coefficients across various subbands and scales, in a medical ultrasound (US) image. The original SPIHT algorithm codes all the subbands with same precision irrespective of their significance, whereas the modified algorithm processes significant subbands with more precision and ignores the least significant subbands. The statistical analysis shows that most of the image energy in ultrasound images lies in the coefficients of vertical detail subbands while diagonal subbands contribute negligibly towards total image energy. Based on these statistical observations, this work presents a new modified SPIHT algorithm, which codes the vertical subbands with more precision while neglecting the diagonal subbands. This modification speeds up the coding/decoding process as well as improving the quality of the reconstructed medical image at low bit rates. The experimental results show that the proposed method outperforms the original SPIHT on average by 1.4 dB at the matching bit rates when tested on a series of medical ultrasound images. Further, the proposed algorithm needs 33% less memory as compared to the original SPIHT algorithm.     

基于功率谱特征的心电数据小波压缩方法

利用心电功率谱特征,探索心电数据压缩新方法。用小波分解心电信号为高频与低频分量,对低频分量继续分解达到要求的级数,对高频分量则根据其所在频段的能量,对临床诊断的价值加以取舍。对MIT生理信号数据库心电数据的压缩与还原分析表明,该方法平衡了压缩比与还原精度之间的矛盾,既具有较高的压缩比,又具有较高的还原精度,而且对信号的适应性也明显增强。另外,该压缩方法还具有一定的去噪作用。说明结合心电功率谱特征与小波变换方法压缩心电有其优势。     

一种基于小波域主成分分析的心电压缩算法

目的为降低心电信号存储和传输的数据量,并克服传统心电压缩方法只利用导联内相关性的劣势,本文提出一种基于小波域主成分分析和分层编码(w PCA_LC)的压缩方法。方法首先通过心电电极获取12通道心电数据,对所有通道的心电信号做小波变换,每个尺度下的小波系数组成小波系数矩阵,在每个系数矩阵上做主成分分析(principal component analysis,PCA),之后对小波系数小的主成分做[位置增量,数据]的编码方式,其他主成分采用霍夫曼编码,最后使用本文算法压缩圣彼得堡心率失常数据库。结果实验表明,在均方根误差为5.2%时,本文算法的压缩比为71,远高于基于稀疏分解的方法和基于小波变换阈值选择的方法。结论基于小波域主成分分析的心电压缩算法对多导联心电信号具有较好的压缩性能。     

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。     

Neural classification of lung sounds using wavelet coefficients

Electronic auscultation is an efficient technique to evaluate the condition of respiratory system using lung sounds. As lung sound signals are non-stationary, the conventional method of frequency analysis is not highly successful in diagnostic classification. This paper deals with a novel method of analysis of lung sound signals using wavelet transform, and classification using artificial neural network (ANN). Lung sound signals were decomposed into the frequency subbands using wavelet transform and a set of statistical features was extracted from the subbands to represent the distribution of wavelet coefficients. An ANN based system, trained using the resilient back propagation algorithm, was implemented to classify the lung sounds to one of the six categories: normal, wheeze, crackle, squawk, stridor, or rhonchus.     

基于小波变换的充气法测量血压的算法研究

目的探索腕部充气测量血压的方法及原理,实现小波变换提取脉搏波以及无创血压的计算,为现有腕式电子血压计提供更精确的算法。方法采用小波变换对采集的袖带脉搏压力混合信号进行去噪处理,并分离脉搏波与袖带压。在此基础上采用差分法与阈值法寻找脉搏波峰值点并修正波形干扰点,再对脉搏波峰值点进行高斯曲线拟合法拟合出平滑包络线,并采用改进的幅度系数法进行收缩压、舒张压的计算,并对30名测试者用本方法与听诊法进行对比测试,观察相关性。结果该方法的测试结果与听诊法对比相关性良好,测量血压速度快,舒适度高。结论基于小波变换的充气法测量血压的算法相比传统方法去噪效果好,脉搏波提取精确度高,血压计算结果符合AAMI标准,但需在软件处理算法中做进一步简化研究。     

Morphological modeling of cardiac signals based on signal decomposition

In this paper a general framework is presented for morphological modeling of cardiac signals from a signal decomposition perspective. General properties of a desired morphological model are presented and special cases of the model are studied in detail. The presented approach is studied for modeling the morphology of electrocardiogram (ECG) signals. Specifically, three types of ECG modeling techniques, including polynomial spline models, sinusoidal model and a model previously presented by McSharry et al., are studied within this framework. The proposed method is applied to datasets from the PhysioNet ECG database for compression and modeling of normal and abnormal ECG signals. Quantitative and qualitative results of these applications are also presented and discussed.     

脑电棘波识别和噪声消除的小波变换方法

研究了利用二进小波变的的模极大值识别脑电信号奇异点如棘波和消除噪声的方法,该方法在较好保留原脑电信号奇异信息的同时能有效地消除噪声,进一步讨论了信号与白噪声的奇异性指数的区别,以及小波变换模极大值沿各变换尺度传递的不同特性,并利用该特性区分信号中的奇异点和噪声,能准确识别奇异点的位置,这种奇异性识别技术在信号的特征提取和消除噪声方面有广阔的应用前景。     

基于小波变换和矢量量化的二维ECG数据压缩算法

本研究针对心电数据的压缩问题,提出了一种新的基于小波变换的二维心电（ECG）数据压缩算法。该算法首先将一维原始ECG信号转化为二维序列信号,从而使ECG数据的两种相关性可得到充分地利用;然后对二维ECG序列进行小波变换,并对变换后的系数应用了一种改进的矢量量化（VQ）方法。在改进的VQ方法中,根据小波变换后系数的特点,构造了一种新的树矢量（TV）。利用本算法与已有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：本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,具有一定的应用价值。     

基于小波包变换的癫痫脑电棘波检测

为了能够较好地实现癫痫患者脑电的棘波检测,提出一种将棘波物理特征（幅度、频率）和小波包变换结合的算法,用于癫痫患者脑电信号的棘波检测。首先利用小波包变换对癫痫脑电信号进行小波包分解,将脑电波频率（0~30 Hz）划分为3层;其次根据脑电波的频率范围重构第三层节点频率S（3, 0）（0~10.85 Hz）、S（3, 1）（10.85~21.7 Hz）、S（3, 2）（21.7~32.55 Hz）的脑电信号;最后取棘波的幅度作为检测阈值分别提取癫痫患者健康期、癫痫发作间期及癫痫发作期的棘波。实验结果证明,当数据的采样频率为173.61 Hz、信号长度为23.6 s时,该算法能够提取不同癫痫患者在不同时期的棘波信号,该算法棘波的误检率为12.02%、漏检率为11.70%。因此,本文所采用的算法在癫痫棘波检测中具有良好的效果。     

基于复小波域非线性扩散的超声图像去噪

超声图像易受斑点噪声的干扰,限制了其在医学诊断中的进一步应用。提出了一种将双树复小波变换(DT-CWT)与非线性扩散相结合的超声图像去噪方法。首先,对图像进行双树复小波分解;然后,高频部分和低频部分分别采用自适应对比度扩散和全变差扩散,最后重构图像。给出了实验结果,并与小波阈值收缩和全变差扩散结合的方法、基于小波和基于多小波的非线性扩散方法的图像去噪效果进行了比较。结果表明,本文提出的方法去噪效果更为优越:不但抑制噪声的能力更强,而且能够更好地保留超声图像原有的边缘和纹理特征。     

小波变换在医学图像增强的应用

利用小波变换对MRI医学图像进行增强处理，使原图像中较模糊、对比度差的细节得到增强，其纹理清晰，处理结果优于传统的直方图均衡和Laplace锐化等图像增强方法。     

Analysis of the pathological severity degree of aortic stenosis (AS) and mitral stenosis (MS) using the discrete wavelet transform (DWT)

The heart is the principal organ that circulates blood. In normal conditions it produces four sounds for each cardiac cycle. However, most often only two sounds appear essential: S1 and S2. Two other sounds: S3 and S4, with lower amplitude than S1 or S2, appear occasionally in the cardiac cycle by the effect of disease or age. The presence of abnormal sounds in one cardiac cycle provide valuable information on various diseases. The aortic stenosis (AS), as being a valvular pathology, is characterized by a systolic murmur due to a narrowing of the aortic valve. The mitral stenosis (MS) is characterized by a diastolic murmur due to a reduction in the mitral valve. Early screening of these diseases is necessary; it’s done by a simple technique known as: phonocardiography. Analysis of phonocardiograms signals using signal processing techniques can provide for clinicians useful information considered as a platform for significant decisions in their medical diagnosis. In this work two types of diseases were studied: aortic stenosis (AS) and mitral stenosis (MS). Each one presents six different cases. The application of the discrete wavelet transform (DWT) to analyse pathological severity of the (AS and MS was presented. Then, the calculation of various parameters was performed for each patient. This study examines the possibility of using the DWT in the analysis of pathological severity of AS and MS.     

Adaptive estimation of QRS complex wave features of ECG signal by the hermite model

The most characteristic wave set in ECG signals is the QRS complex. Automatic procedures to classify the QRS are very useful in the diagnosis of cardiac dysfunctions. Early detection and classification of QRS changes are important in realtime monitoring. ECG data compression is also important for storage and data transmission. An Adaptive Hermite Model Estimation System (AHMES) is presented for on-line beat-to-beat estimation of the features that describe the QRS complex with the Hermite model. The AHMES is based on the multiple-input adaptive linear combiner, using as inputs the succession of the QRS complexes and the Hermite functions, where a procedure has been incorporated to adaptively estimate a width related parameter b. The system allows an efficient real-time parameter extraction for classification and data compression. The performance of the AHMES is compared with that of direct feature estimation, studying the improvement in signal-to-noise ratio. In addition, the effect of misalignment at the QRS mark is shown to become a neglecting low-pass effect. The results allow the conditions in which the AHMES improves the direct estimate to be established. The application is shown, for subsequent classification, of the AHMES in extracting the QRS features of an ECG signal with the bigeminy phenomena. Another application is highlighted that helps wide ectopic beats detection using the width parameter b.