Denoising performance of modified dual-tree complex wavelet transform for processing quadrature embolic Doppler signals

Quadrature signals are dual-channel signals obtained from the systems employing quadrature demodulation. Embolic Doppler ultrasound signals obtained from stroke-prone patients by using Doppler ultrasound systems are quadrature signals caused by emboli, which are particles bigger than red blood cells within circulatory system. Detection of emboli is an important step in diagnosing stroke. Most widely used parameter in detection of emboli is embolic signal-to-background signal ratio. Therefore, in order to increase this ratio, denoising techniques are employed in detection systems. Discrete wavelet transform has been used for denoising of embolic signals, but it lacks shift invariance property. Instead, dual-tree complex wavelet transform having near-shift invariance property can be used. However, it is computationally expensive as two wavelet trees are required. Recently proposed modified dual-tree complex wavelet transform, which reduces the computational complexity, can also be used. In this study, the denoising performance of this method is extensively evaluated and compared with the others by using simulated and real quadrature signals. The quantitative results demonstrated that the modified dual-tree-complex-wavelet-transform-based denoising outperforms the conventional discrete wavelet transform with the same level of computational complexity and exhibits almost equal performance to the dual-tree complex wavelet transform with almost half computational cost.     

Enhanced T-ray signal classification using wavelet preprocessing

This study demonstrates the application of one-dimensional discrete wavelet transforms in the classification of T-ray pulsed signals. Fast Fourier transforms (FFTs) are used as a feature extraction tool and a Mahalanobis distance classifier is employed for classification. Soft threshold wavelet shrinkage de-noising is used and plays an important role in de-noising and reconstruction of T-ray pulsed signals. An iterative algorithm is applied to obtain three optimal frequency components and to achieve preferred classification performance.     

The effectiveness of the wavelet transforms method in the heart sounds analysis

Heart sounds can be used more efficiently by medical doctors when they are displayed visually, rather through a conventional stethoscope. Heart sounds provide clinicians with valuable diagnostic and prognostic information. Although heart sound analysis by auscultation is convenient as a clinical tool, heart sound signals are so complex and non-stationary that they are very difficult to analyse in time or frequency domains. We have studied the extraction of features from heart sounds in the time-frequency domain for recognition of heart sounds through time-frequency analysis. The application of wavelet transform for the heart sounds is thus described. The performance of continuous wavelet transform, discrete wavelet transform and packet wavelet transform is discussed in this paper. After these transformations, we can compare normal and abnormal heart sounds to verify clinical usefulness of our extraction methods for recognition of heart sounds.     

Biomedical signal processing (in four parts)

This is the second in a series of four tutorial papers on biomedical signal processing, and it concerns the relationships between commonly used frequency transforms. It begins with the Fourier series and Fourier transform for continuous time signals and extends these concepts for aperiodic discrete time data and then periodic discrete time data. The Laplace transform is discussed as an extension of the Fourier transform. The z-transform is introduced and the ideas behind the chirp-z transform are described. The equivalence between the time and frequency domains is described in terms of Parseval's theorem and the theory of convolution. The use of the FFT for fast convolution and fast correlation is described for both short recordings and long recordings that must be processed in sections.     

Image Texture Characterization Using the Discrete Orthonormal S-Transform

We present a new efficient approach for characterizing image texture based on a recently published discrete, orthonormal space-frequency transform known as the DOST. We develop a frequency-domain implementation of the DOST in two dimensions for the case of dyadic frequency sampling. Then, we describe a rapid and efficient approach to obtain local spatial frequency information for an image and show that this information can be used to characterize the horizontal and vertical frequency patterns in synthetic images. Finally, we demonstrate that DOST components can be combined to obtain a rotationally invariant set of texture features that can accurately classify a series of texture patterns. The DOST provides the computational efficiency and multi-scale information of wavelet transforms, while providing texture features in terms of Fourier frequencies. It outperforms leading wavelet-based texture analysis methods.     

基于自适应局部余弦变换的正交多普勒超声信号降噪方法的研究

多普勒超声信号的谱图已经被广泛用于医疗诊断。来自系统内部的噪声及外部的干扰会产生附加的频谱成分，从而影响谱图的主观分析及进一步的定量分析。为抑制噪声的影响，本文提出利用一种新的基于自适应局部余弦变换和非负Garrote取阈值的方法对正交多普勒超声信号进行降噪。首先，由正交信号提取正向和逆向血流信息；然后对其分别进行降噪；最后利用Hilbert变换进行重构得到真实信号的估计。在仿真研究中，采用平均频率波形和谱宽波形的估计精度作为性能改善的指标。结果表明这种方法优于基于小波变换的降噪方法，特别是在低信噪比情况下。     

基于小波包分解的不同状态下脑电信号分析

本文针对脑电信号的非平稳性,引入小波包分解理论处理临床脑电.根据脑电信号的不同节律特性,提出应用小波包分解构造不同频率特性的时变滤波器,提取脑电信号不同节律的动态特性,并由此构造各种节律的动态脑电地形图.为了研究不同脑功能状态下脑电信号各种节律的动态特性,文中对两组不同的临床脑电数据进行分析,比较两种状态下各种节律的动态特性.实验结果表明,利用小波包分解对脑电信号进行滤波,能够有效提取临床脑电不同节律的动态特性,为分析脑电信号提供一条新的途径.     

癫痫脑电特征波的综合检测分类方法研究

本文将小波变换、人工神经网络、专家规则判据等多种检测方法有机地结合起来 ,用于癫痫脑电特征波的检测与分类 ,以充分发挥不同方法的优势。这种综合检测分类方法是先将预处理的多导脑电时间序列经小波变换将脑电中癫痫特征波在不同尺度下分离出来 ,再对选出的癫痫嫌疑波进行特征参数提取 ,然后把特征参数送入已经训练好的人工神经网络进行分类识别 ,最后再由专家规则判断筛选并作出检测分类统计报告。研究表明 ,该方法具有很好的信号特征提取和屏蔽随机噪声能力 ,获得了较好的检出率 ;尤其适合于非平稳、非线性生物医学信号的检测分类 ,值得进一步深入研究     

Automated wavelet denoising of photoacoustic signals for circulating melanoma cell detection and burn image reconstruction

Photoacoustic image reconstruction may involve hundreds of point measurements, each of which contributes unique information about the subsurface absorbing structures under study. For backprojection imaging, two or more point measurements of photoacoustic waves induced by irradiating a biological sample with laser light are used to produce an image of the acoustic source. Each of these measurements must undergo some signal processing, such as denoising or system deconvolution. In order to process the numerous signals, we have developed an automated wavelet algorithm for denoising signals. We appeal to the discrete wavelet transform for denoising photoacoustic signals generated in a dilute melanoma cell suspension and in thermally coagulated blood. We used 5, 9, 45 and 270 melanoma cells in the laser beam path as test concentrations. For the burn phantom, we used coagulated blood in 1.6 mm silicon tube submerged in Intralipid. Although these two targets were chosen as typical applications for photoacoustic detection and imaging, they are of independent interest. The denoising employs level-independent universal thresholding. In order to accommodate nonradix-2 signals, we considered a maximal overlap discrete wavelet transform (MODWT). For the lower melanoma cell concentrations, as the signal-to-noise ratio approached 1, denoising allowed better peak finding. For coagulated blood, the signals were denoised to yield a clean photoacoustic resulting in an improvement of 22% in the reconstructed image. The entire signal processing technique was automated so that minimal user intervention was needed to reconstruct the images. Such an algorithm may be used for image reconstruction and signal extraction for applications such as burn depth imaging, depth profiling of vascular lesions in skin and the detection of single cancer cells in blood samples.     

Epileptic seizure detection using DWT-based approximate entropy,Shannon entropy and support vector machine: a case study

In this work, we have used a time–frequency domain analysis method called discrete wavelet transform (DWT) technique. This method stand out compared to other proposed methods because of its algorithmic elegance and accuracy. A wavelet is a mathematical function based on time-frequency analysis in signal processing. It is useful particularly because it allows a weak signal to be recovered from a noisy signal without much distortion. A wavelet analysis works by analysing the image and converting it to mathematical function which is decoded by the receiver. Furthermore, we have used Shannon entropy and approximate entropy (ApEn) for extracting the complexities associated with electroencephalographic (EEG) signals. The ApEn is a suitable feature to characterise the EEGs because its value drops suddenly due to excessive synchronous discharge of neurons in the brain during epileptic activity in this study. EEG signals are decomposed into six EEG sub-bands namely D1–D5 and A5 using DWT technique. Non-linear features such as ApEn and Shannon entropy are calculated from these sub-bands and support vector machine classifiers are used for classification purpose. This scheme is tested using EEG data recorded from five healthy subjects and five epileptic patients during the inter-ictal and ictal periods. The data are acquired from University of Bonn, Germany. The proposed method is evaluated through 15 classification problems, and obtained high classification accuracy of 100% for two cases and it indicates the good classifying performance of the proposed method.     

A new local multiscale Fourier analysis for medical imaging

The Stockwell transform (ST), recently developed for geophysics, combines features of the Fourier, Gabor and wavelet transforms; it reveals frequency variation over time or space. This valuable information is obtained by Fourier analysis of a small segment of a signal at a time. Localization of the Fourier spectrum is achieved by filtering the signal with frequency-dependent Gaussian scaling windows. This multi-scale time-frequency analysis provides information about which frequencies occur and more importantly when they occur. Furthermore, the Stockwell domain can be directly inferred from the Fourier domain and vice versa. These features make the ST a potentially effective tool to visualize, analyze, and process medical imaging data. The ST has proven useful in noise reduction and tissue texture analysis. Herein, we focus on the theory and effectiveness of the ST for medical imaging. Its effectiveness and comparison with other linear time-frequency transforms, such as the Gabor and wavelet transforms, are discussed and demonstrated using functional magnetic resonance imaging data.     

Detection of arterial disorders by spectral analysis techniques

This paper intends to an integrated view of the spectral analysis techniques in the detection of arterial disorders. The paper includes illustrative information about feature extraction from signals recorded from arteries. Short-time Fourier transform (STFT) and wavelet transform (WT) were used for spectral analysis of ophthalmic arterial (OA) 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 OA stenosis. The author suggest that the content of the paper will assist to the people in gaining a better understanding of the STFT and WT in the detection of arterial disorders.     

Wavelets in biomedical engineering

Wavelets analysis methods have been widely used in the signal processing of biomedical signals. These methods represent the temporal characteristics of a signal by its spectral components in the frequency domain. In this way, important features of the signal can be extracted in order to understand or model the physiological system. This paper reviews the widely used orthogonal wavelet transform method in the biomedical applications.     

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.     

Comparison of STFT and wavelet transform methods in determining epileptic seizure activity in EEG signals for real-time application

Electroencephalography (EEG) is widely used in clinical settings to investigate neuropathology. Since EEG signals contain a wealth of information about brain functions, there are many approaches to analyzing EEG signals with spectral techniques. In this study, the short-time Fourier transform (STFT) and wavelet transform (WT) were applied to EEG signals obtained from a normal child and from a child having an epileptic seizure. For this purpose, we developed a program using Labview software. Labview is an application development environment that uses a graphical language G, usable with an online applicable National Instruments data acquisition card. In order to obtain clinically interpretable results, frequency band activities of delta, theta, alpha and beta signals were mapped onto frequency-time axes using the STFT, and 3D WT representations were obtained using the continuous wavelet transform (CWT). Both results were compared, and it was determined that the STFT was more applicable for real-time processing of EEG signals, due to its short process time. However, the CWT still had good resolution and performance high enough for use in clinical and research settings.     

A Modified Undecimated Discrete Wavelet Transform Based Approach to Mammographic Image Denoising

In this work, the authors present an effective denoising method to attempt reducing the noise in mammographic images. The method is based on using hierarchical correlation of the coefficients of discrete stationary wavelet transforms. The features of the proposed technique include iterative use of undecimated multi-directional wavelet transforms at adjacent scales. To validate the proposed method, computer simulations were conducted, followed by its applications to clinical mammograms. Mutual information originating from information theory was used as an evaluation measure for selection of an optimal wavelet basis function. We examined the performance of the proposed method by comparing it with the conventional undecimated discrete wavelet transform (UDWT) method in terms of processing time-consuming and image quality. Our results showed that with the use of the proposed method the computation time can be reduced to approximately 1/10 of the conventional UDWT method consumed. The results of visual assessment indicated that the images processed with the proposed UDWT method showed statistically significant superior image quality over those processed with the conventional UDWT method. Our research results demonstrate the superiority and effectiveness of the proposed approach.     

Beta (~16 Hz) frequency neural oscillations mediate auditory sensory gating in humans

The brain's oscillatory activities in response to sensory input are likely signals representing different stages of sensory information processing. To understand these signals, it is critical to establish the specificity of the timing and frequency of oscillations associated with sensory and sensory-related cognitive processing. We used a simple paired auditory stimulus paradigm for sensory gating and sought to identify time- and frequency-specific oscillatory components contributing to sensory gating. Using a discrete wavelet decomposition technique we separated single-trial time-frequency components of evoked potentials elicited by the first of two stimuli. Regression analyses were then used to identify the components most relevant to the suppression of the second evoked potential response. The results suggested that beta oscillation indexed a neural process associated with the strength of sensory gating.     

The application of Hilbert�CHuang transform in the analysis of muscle fatigue during cyclic dynamic contractions

Surface electromyography (sEMG) is a common technique used in the assessment of local muscle fatigue. As opposed to static contraction situations, sEMG recordings during dynamic contractions are particularly characterised by non-stationary (and non-linear) features. Standard signal processing methods using Fourier and wavelet based procedures demonstrate well known restrictions on time-frequency resolution and the ability to process non-stationary and/or non-linear time-series, thus aggravating the spectral parameters estimation. The Hilbert-Huang transform (HHT), comprising of the empirical mode decomposition (EMD) and Hilbert spectral analysis (HSA), provides a new approach to overcome these issues. The time-dependent median frequency estimate is used as muscle fatigue indicator, and linear regression parameters are derived as fatigue quantifiers. The HHT method is utilised for the analysis of the sEMG signals recorded over quadriceps muscles during cyclic dynamic contractions. The results are compared with those obtained by the Fourier and wavelet based methods. It is shown that HHT procedure provides the most consistent and reliable assessment of spectral and derived linear regression parameters, given the time epoch width and sampling interval in the time domain. The suggested procedure successfully deals with non-stationary and non-linear properties of biomedical signals.     

基于小波的海洛因成瘾者脉象异常分析

利用小波变换的方法，对15例海洛因成瘾者和15例正常人的脉搏信号的时频特征予以分析，根据用于显示离散二进小波变换的三维图形及等高线图，海洛因成瘾者与正常人脉象信号间时频特征的显著差异予以揭示，由此得出了初步的判据。根据该判据，15例海洛因成瘾者全部被检测出来，正常人有2例误检。研究结果表明，基于小波的多分辨率分析是提取脉搏信号特征的一种非常有效的方法。本文对于海洛因成瘾者的诊疗具有一定的价值。     

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。