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.     

Analysis of phonocardiogram signals using wavelet transform

Phonocardiograms (PCG) are recordings of the acoustic waves produced by the mechanical action of the heart. They generally consist of two kinds of acoustic vibrations: heart sounds and heart murmurs. Heart murmurs are often the first signs of pathological changes of the heart valves, and are usually found during auscultation in primary health care. Heart auscultation has been recognized for a long time as an important tool for the diagnosis of heart disease, although its accuracy is still insufficient to diagnose some heart diseases. It does not enable the analyst to obtain both qualitative and quantitative characteristics of the PCG signals. The efficiency of diagnosis can be improved considerably by using modern digital signal processing techniques. Therefore, these last can provide useful and valuable information on these signals. The aim of this study is to analyse PCG signals using wavelet transform. This analysis is based on an algorithm for the detection of heart sounds (the first and second sounds, S1 and S2) and heart murmurs using the PCG signal as the only source. The segmentation algorithm, which separates the components of the heart signal, is based on denoising by wavelet transform (DWT). This algorithm makes it possible to isolate individual sounds (S1 or S2) and murmurs. Thus, the analysis of various PCGs signals using wavelet transform can provide a wide range of statistical parameters related to the phonocardiogram signal.     

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.     

An ECG signal compressor based on the selection of optimal threshold levels of discrete wavelet transform coefficients

Compression of electrocardiography (ECG) is necessary for efficient storage and transmission of the digitized ECG signals. Discrete wavelet transform (DWT) has recently emerged as a powerful technique for ECG signal compression due to its multi-resolution signal decomposition and locality properties. This paper presents an ECG compressor based on the selection of optimum threshold levels of DWT coefficients in different subbands that achieve maximum data volume reduction while preserving the significant signal morphology features upon reconstruction. First, the ECG is wavelet transformed into m subbands and the wavelet coefficients of each subband are thresholded using an optimal threshold level. Thresholding removes excessively small features and replaces them with zeroes. The threshold levels are defined for each signal so that the bit rate is minimized for a target distortion or, alternatively, the distortion is minimized for a target compression ratio. After thresholding, the resulting significant wavelet coefficients are coded using multi embedded zero tree (MEZW) coding technique. In order to assess the performance of the proposed compressor, records from the MIT-BIH Arrhythmia Database were compressed at different distortion levels, measured by the percentage rms difference (PRD), and compression ratios (CR). The method achieves good CR values with excellent reconstruction quality that compares favourably with various classical and state-of-the-art ECG compressors. Finally, it should be noted that the proposed method is flexible in controlling the quality of the reconstructed signals and the volume of the compressed signals by establishing a target PRD and a target CR a priori, respectively.     

Powerline interference reduction in ECG signals using empirical wavelet transform and adaptive filtering

Abstract

Separating an information-bearing signal from the background noise is a general problem in signal processing. In a clinical environment during acquisition of an electrocardiogram (ECG) signal, The ECG signal is corrupted by various noise sources such as powerline interference (PLI), baseline wander and muscle artifacts. This paper presents novel methods for reduction of powerline interference in ECG signals using empirical wavelet transform (EWT) and adaptive filtering. The proposed methods are compared with the empirical mode decomposition (EMD) based PLI cancellation methods. A total of six methods for PLI reduction based on EMD and EWT are analysed and their results are presented in this paper. The EWT-based de-noising methods have less computational complexity and are more efficient as compared with the EMD-based de-noising methods.     

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。     

应用小波变换进行心音三维时频分析的研究

应用小波变换分析方法,对正常人和典型心脏病人的心音数据分组进行不同尺度的小波变换,得出了综合反映心音的时间,频率和强度三维信息的彩色三维心音图,经小波变换的三维心音图更为直观详尽地反映出各组心音信号在不同时域、频域与强度范围内细节性的信息。对提取的时频参数进行分析,各组间存在显著性差异,该研究为临床心脏病诊断和辅助教学提供了一个有效的工具,为心音分析的进一步提供了基础资料与方法。     

Spectral broadening of ophthalmic arterial Doppler signals using STFT and wavelet transform

In this study, short-time Fourier transform (STFT) and wavelet transform (WT) were used for spectral analysis of ophthalmic arterial Doppler signals. Using these spectral analysis methods, the variations in the shape of the Doppler spectra as a function of time were presented in the form of sonograms in order to obtain medical information. These sonograms were then used to compare the applied methods in terms of their frequency resolution and the effects in determination of spectral broadening in the presence of ophthalmic artery stenosis. A qualitative improvement in the appearance of the sonograms obtained using the WT over the STFT was noticeable. Despite the qualitative improvement in the individual sonograms, no quantitative advantage in using the WT over the STFT for the determination of spectral broadening index was obtained due to the poorer variance of the wavelet transform-based spectral broadening index and the additional computational requirements of the wavelet transform.     

Bileaflet mechanical valve sound analysis using a continuous wavelet transform

Bileaflet mechanical valve closing sounds have splits, the duration of which is not constant in normally functioning valves. However, no reports have discussed the influences of valve malfunction on the split interval, neither have any studies discussed the fact that mechanical valve closing sound signals must be analyzed using a time-frequency analysis because they are nonstationary signals. The continuous wavelet transform (CWT), a time-frequency analyzing method using mother wavelets modified by scale numbers, was selected in this study for analyzing bileaflet valve closing sounds because it is easy to understand and has no limitations such as the cross-terms in the Wigner–Ville distribution or the tradeoff between time and frequency resolutions of the short-time Fourier transform. This study compares the properties of the mother wavelets of various CWTs and selects one that is suitable for detection of the clear split in bileaflet mechanical valve closing sound signals. This article also establishes a standard frequency analyzing system for bileaflet mechanical valve sounds. A preliminary study with chirp Doppler signals for comparing the frequency properties of the mother wavelets of various CWTs suggested that Ishikawa's modified Morlet CWT has better time and frequency resolution at the highest frequency scale. Morlet/power CWT analysis of normal in vivo bileaflet valve closing sounds of the ST. Jude Medical (SJM), ATS, and Carbomedics (CM) valves demonstrated clear splits of very short interval at the highest level of frequency. Detection of the disappearance of the split by using this analytical method may be the key to identifying bileaflet mechanical valve malfunction in outpatient departments.     

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 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.     

Time delay estimation using wavelet transform for pulsed-wave ultrasound

The windowed cross-correlation (WCC) technique has recently attracted attention in pulsed-wave (PW) ultrasound for measurement of tissue motion and blood flow velocity because of its performance advantages over the conventional Doppler method. The WCC measures tissue motion and blood flow velocity via estimation of time delays of backscattered signals in two consecutive echoes. In this paper, we propose a wavelet transform-based cross-correlation (WTCC) technique for the time delay estimation in PW ultrasound. The WTCC consists of three steps: (i) computing wavelet transforms (WTs) of received echoes, (ii) computing cross-correlations in the wavelet domain, and (iii) estimating the time delays by maximizing the estimated cross-correlations. Dyadic or continuous wavelets may be used in the proposed approach. The WTCC has a unique feature of using varying time-frequency windows in processing compared with the WCC which only uses a single fixed window. Our computer simulations show that, compared with the WCC, the WTCC provides a better estimation of time delays (lower failure rate and lower estimate error) and its performance is more consistent under various conditions, and more robust with window size. In the simulations, we also tested a specific continuous wavelet for the WTCC that was the emitted pulse itself and found the corresponding WTCC outperforms the WTCC with a regular dyadic wavelet.     

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.     

Investigating routes to chaos in the guinea-pig cochlea using the continuous wavelet transform and the short-time Fourier transform

The continuous wavelet transform (CWT) and the short-time Fourier transform (STFT) were used to analyze the time course of cellular motion in the guinea pig inner ear. The velocity responses of individual outer hair cells and Hensen's cells to amplitude modulated (AM) acoustical signals applied to the ear canal displayed characteristics typical of nonlinear systems, such as the generation of spectral components at harmonics of the carrier frequency. Nonlinear effects were particularly pronounced at the highest stimulus levels, where half-harmonic (and sometimes quarter-harmonic) components were also seen. The generation of these components was consistent with the behavior of a dynamical system entering chaos via a period-doubling route. A negative-stiffness Duffing oscillator model yielded period-doubling behavior similar to that of the experimental data. We compared the effectiveness of the CWT and the STFT for analyzing the responses to AM stimuli. The CWT (calculated using a high-Q Morlet-wavelet basis) and the STFT were both useful for identifying the various spectral components present in the AM velocity response of the cell. The high-Q Morlet wavelet CWT was particularly effective in distinguishing the lowest frequency components present in the response, since its frequency resolution is appreciably better than the STFT at low frequencies. Octave-band-based CWTs (using low-Q Morlet, Meyer, and Daubechies 4-tap wavelets) were largely ineffective in analyzing these signals, inasmuch as the frequency spacing between neighboring spectral components was far less than one octave.     

Hidden Markov model based epileptic seizure detection using tunable Q wavelet transform

Epilepsy is one of the most prevalent neurological disorders affecting 70 million people worldwide. The present work is focused on designing an efficient algorithm for automatic seizure detection by using electroencephalogram (EEG) as a noninvasive procedure to record neuronal activities in the brain. EEG signals' underlying dynamics are extracted to differentiate healthy and seizure EEG signals. Shannon entropy, collision entropy, transfer entropy, conditional probability, and Hjorth parameter features are extracted from subbands of tunable Q wavelet transform. Efficient decomposition level for different feature vector is selected using the Kruskal-Wallis test to achieve good classification. Different features are combined using the discriminant correlation analysis fusion technique to form a single fused feature vector. The accuracy of the proposed approach is higher for Q=2 and J=10. Transfer entropy is observed to be significant for different class combinations. Proposed approach achieved 100% accuracy in classifying healthy-seizure EEG signal using simple and robust features and hidden Markov model with less computation time. The proposed approach efficiency is evaluated in classifying seizure and non-seizure surface EEG signals. The system has achieved 96.87% accuracy in classifying surface seizure and nonseizure EEG segments using efficient features extracted from different J level.     

Application of adaptive neuro-fuzzy inference system for epileptic seizure detection using wavelet feature extraction

Intelligent computing tools such as artificial neural network (ANN) and fuzzy logic approaches are demonstrated to be competent when applied individually to a variety of problems. Recently, there has been a growing interest in combining both these approaches, and as a result, neuro-fuzzy computing techniques have been evolved. In this study, a new approach based on an adaptive neuro-fuzzy inference system (ANFIS) was presented for epileptic seizure detection. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Decision making was performed in two stages: feature extraction using the wavelet transform (WT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Some conclusions concerning the impacts of features on the detection of epileptic seizures were obtained through analysis of the ANFIS. The results are highly promising, and a comparative analysis suggests that the proposed modeling approach outperforms ANN model in terms of training performances and classification accuracies. The results confirmed that the proposed ANFIS model has some potential in epileptic seizure detection. The ANFIS model achieved accuracy rates which were higher than that of the stand-alone neural network model.     

Fuzzy support vector machine for classification of EEG signals using wavelet-based features

Translation of electroencephalographic (EEG) recordings into control signals for brain–computer interface (BCI) systems needs to be based on a robust classification of the various types of information. EEG-based BCI features are often noisy and likely to contain outliers. This contribution describes the application of a fuzzy support vector machine (FSVM) with a radial basis function kernel for classifying motor imagery tasks, while the statistical features over the set of the wavelet coefficients were extracted to characterize the time–frequency distribution of EEG signals. In the proposed FSVM classifier, a low fraction of support vectors was used as a criterion for choosing the kernel parameter and the trade-off parameter, together with the membership parameter based solely on training data. FSVM and support vector machine (SVM) classifiers outperformed the winner of the BCI Competition 2003 and other similar studies on the same Graz dataset, in terms of the competition criterion of the mutual information (MI), while the FSVM classifier yielded a better performance than the SVM approach. FSVM and SVM classifiers perform much better than the winner of the BCI Competition 2005 on the same Graz dataset for the subject O3 according to the competition criterion of the maximal MI steepness, while the FSVM classifier outperforms the SVM method. The proposed FSVM model has potential in reducing the effects of noise or outliers in the online classification of EEG signals in BCIs.     

Heart sound reproduction based on neural network classification of cardiac valve disorders using wavelet transforms of PCG signals

Cardiac auscultatory proficiency of physicians is crucial for accurate diagnosis of many heart diseases. Plenty of diverse abnormal heart sounds with identical main specifications and different details representing the ambient noise are indispensably needed to train, assess and improve the skills of medical students in recognizing and distinguishing the primary symptoms of the cardiac diseases. This paper proposes a versatile multiresolution wavelet-based algorithm to first extract the main statistical characteristics of three well-known heart valve disorders, namely the aortic insufficiency, the aortic stenosis, and the pulmonary stenosis sounds as well as the normal ones. An artificial neural network (ANN) and statistical classifier are then applied alternatively to choose proper exclusive features. Both classification approaches suggest using Daubechies wavelet filter with four vanishing moments within five decomposition levels for the most prominent distinction of the diseases. The proffered ANN is a multilayer perceptron structure with one hidden layer trained by a back-propagation algorithm (MLP-BP) and it elevates the percentage classification accuracy to 94.42. Ultimately, the corresponding main features are manipulated in wavelet domain so as to sequentially regenerate the individual counterparts of the underlying signals.