基于离散小波变换的fMRI数据特征提取

目的 设计一种灵敏度高且处理速度快的fMRI数据小波分析方法.方法 先用离散小波变换和频谱分析确定有用信号存在的小波分解尺度,也即特征尺度;再对实验数据进行离散小波分解,重构时将非特征尺度里的小波系数设置为0;再以相关分析对小波重构信号进行激活检测.结果 对视觉数据的分析结果显示,新方法的灵敏度与基于平稳小波变换、SPM2方法相当,而优于基于提升小波变换的方法;新方法的处理速度与基于提升小波变换的方法相当,但较平稳小波变换方法有较大提高.结论 本文为fMRI数据提供了一种更为灵敏且快速的小波分析方法,更为实用.     

基于小波变换的功能磁共振数据处理方法

近年来,小波变换在功能磁共振成像(fMRI)数据分析中获得了广泛的应用,小波变换的一些独特的性质使之非常适合于fMRI数据的处理。本文对基于小波变换的fMRI数据分析方法予以综述,指出了基于小波变换的分析方法的优势和局限性,并对这类方法的发展趋势进行了展望。     

基于独立成分和小波分析的胎儿心电提取

背景:在胎儿心电信号的采集过程中,会受到母体和其他噪声的强干扰,如何快捷与有效地提取出胎儿心电将成为重要的研究课题。目的:采用结合独立成分分析和小波分析的方法对来自于同一母体的观测信号进行独立分量分离,得到有效的胎儿心电。方法:结合独立成分分析和小波分析的算法进行胎儿心电的特征提取,首先对含噪信号进行小波变换,去除奇异信号和非平稳随机信号,然后对小波重构后的信号运用快速独立成分分析算法进行成分分析。结果与结论:在胎儿心电信号的采集过程中,会受到母体和其他噪声的强干扰,但这些信号都是随机的,不相关的,可以认为它们间是相互独立的。采用结合独立成分和小波分析的方法对来自于同一母体的观测信号进行独立分量分离,得到有效的胎儿心电。实验证明该方法是一种有效的方法。     

基于独立成分和小波分析的胎儿心电提取

背景：在胎儿心电信号的采集过程中,会受到母体和其他噪声的强干扰,如何快捷与有效地提取出胎儿心电将成为重要的研究课题。目的：采用结合独立成分分析和小波分析的方法对来自于同一母体的观测信号进行独立分量分离,得到有效的胎儿心电。方法：结合独立成分分析和小波分析的算法进行胎儿心电的特征提取,首先对含噪信号进行小波变换,去除奇异信号和非平稳随机信号,然后对小波重构后的信号运用快速独立成分分析算法进行成分分析。结果与结论：在胎儿心电信号的采集过程中,会受到母体和其他噪声的强干扰,但这些信号都是随机的,不相关的,可以认为它们间是相互独立的。采用结合独立成分和小波分析的方法对来自于同一母体的观测信号进行独立分量分离,得到有效的胎儿心电。实验证明该方法是一种有效的方法。     

优化平稳小波变换去除功能MRI数据噪声的方法

目的通过优化小波变换多分辨率去噪方法,在保持一定灵敏度的条件下,降低假阳性率。方法在小波重构时较原来的方法保留更多的小波尺度,对原有方法的分析结果进行多人平均,并用模拟数据和视觉实验数据对这些方法进行验证。结果分析模拟数据显示,在α〈0.01条件下,本文方法能在保持一定灵敏度的基础上有效地克服原有方法假阳性率高的缺点。分析实验数据显示,以SPM2为标准,在α〈0.001条件下,本文方法能给出既灵敏又相对准确的结果。结论本文方法能同时兼顾灵敏度和准确度,是原有方法的一种优化。     

结合小波变换对心电信号中RR间期检测的一种新方法

目的:探讨改善心电信号噪声干扰的方法,提高对心率变异信号中的RR间期检测精度。方法:①采用国际上通用的MIT/BIH数据库作为研究心率变异信号中RR间期的研究对象,选取其中的代表性数据组进行实验分析(受严重基线漂移影响的正常心电信号101.dat,大量室性期前收缩的心电信号105.dat,无噪声干扰的正常心电信号213.dat,心动过速的心电信号217.dat)。②利用小波变化方法结合Mallat算法对含有噪声的心电信号进行多尺度分析和信号重构,并利用R波的自身波形特点,采用相对周期极大值法来进行R波位置检测,进而计算RR间期序列值。结果:利用小波变换对含有噪声的信号进行噪声消除可以达到在很大程度保留原始信号的波形特征的同时又取得良好消噪效果的目的。能够显著减小工频干扰、基线漂移和肌电干扰等噪声对判别的影响。通过MIT/BIH数据库中四组有代表性特征的心电信号进行研究,发现采用相对周期极大值检测法可以显著减少检测中易出现的漏检和误检现象,快速而准确的获得RR间期的序列值。结论:小波变换法能够显著减少噪声对信号的干扰,特别是离散小波的应用使数字信号的处理由理论走向实际,结合Mallat快速算法,使得小波变换完全走向实用化。周期极大值法对心率变异信号中RR间期的检测有较好的精确度和快速性。     

基于经验模式分解与独立分量分析的心电信号消噪方法

针对小波独立分量分析法(W-ICA)在心电信号消噪中小波变换缺乏自适应性,且较难选取最优小波基的问题,提出了一种将经验模式分解与独立分量分析相结合的小波独立分量分析法.该方法结合经验模式分解与独立分量分析各自的优点,利用经验模式分解对心电信号进行自适应分解,然后应用独立分量分析法对选取的本征模态函数进行分离,将分离后的分量进行两层重构,从而得消噪后的心电信号.通过利用MIT-BIH心率失常数据库中的数据进行仿真实验,结果表明该方法可以较好地消除心电信号中的噪声,消噪后信号与原信号的相关系数可达0.96.     

从体表电生理信号中提取希氏束信号的分析系统

背景:传统的希氏束检测方法是对体表心电信号进行数百次叠加或者经食道检测以及心内导管检测得到,研制从体表心电信号提取希氏束信号不但有利于临床诊断,也有利于动物药物实验.目的:从体表心电信号中提取希氏束信号,并开发体表希氏束信号分析系统.方法:以家兔体表心电信号作为待分析信号,以其心内希氏束电图作为对照信号,采用随机共振、小波变换、叠加平均和耦合累加等分析方法,对体表心电信号进行分析.结果与结论:小波变换后得到的信号,可以从体表心电信号中检测出希氏束信号,但并不是所有希氏束信号都能被识别,心内信号经过小波变换后,个别希氏束信号反而消失.随机共振方法从体表心电中检测出的希氏束信号识别率要高于小波分析方法,随机共振方法与小波分析相同之处是,心内信号经过处理后,个别希氏束信号反而消失.本文提出的耦合叠加算法能够从体表心电信号提取出希氏束信号,与经典叠加方法比较,其优点是希氏束信号明显,叠加次数远远少于经典叠加方法.提示实验采用的随机共振、小波变换、耦合累加等分析方法,能够有效抑制噪声、提取希氏束信号,开发研制的体表希氏束信号分析系统具有较强的实用价值.     

基于B-样条双正交小波R波的标定和QRS波检测

实现心电信号QRS波检测的算法很多,文章给出了一种基于B-样条双正交小波对心电信号R波峰值标定和QRS波波段检测的方法.利用双正交样条小波等效滤波器,对心电信号按Mallat算法进行快速变换:从信号奇异点的李氏指数与模极大值关系的角度,分析心电信号奇异点(R峰值点)与其小波变换模极大值对的零交叉点的关系.用二次B-样条小波滤波器组对心电数字信号进行4个尺度的小波分解,然后根据分解的尺度波形特性求出正负极值对过零点,即R波峰值,并枪测出QRS波段.采用Matlab编程实现该算法.从实验结果可以得出,该算法对心电信号中QRS波群的特征提取和几种常见的心电十扰具有较强的鲁棒性,经MIT-BIH标准心律失常数据库验证,QRS波的正确检测率达99.9%.文中给出了程序流程图.     

基于小波极大模值信息的医学图像去噪方法

目的 利用小波变换进行医学图像去噪.方法 通过分析二进小波变换下小波极大模值的特点,即信号的极大模值往往会大于噪声的极大模值,而且噪声的极大模值会随着尺度增大而急剧减少,信号的极大模值却改变很小,由此构造了更有效的去噪准则,即根据不同尺度上的极大模值信息,选择不同的域值来滤除噪声.结果 应用该方法进行医学图像去噪,能保持较高的峰值信噪比、图像细节和边缘特征以及图像清晰度.结论 基于小波极大模值信息的去噪方法能有效地降低医学图像中的噪声.     

功能磁共振图像小波域分析结果的修正

目的改进f MRI数据小波域分析方法。方法通过在原始空间域对传统小波方法检测出的激活区再进行检验来修正传统小波方法的缺点,并以SPM99为标准,通过比较传统小波方法和修正方法对一组手动实验数据的分析结果来说明修正方法的效果。结果修正方法能较好地除去或减小传统小波方法中激活区的扩散和伪影。结论小波域分析f MRI图像是一种快速灵敏的方法,但重建后激活区扩散且有伪影。本文提出的修正方法是一种快速且较传统小波方法准确的f MRI数据分析方法。     

Wavelet variance components in image space for spatiotemporal neuroimaging data

Neuroimaging studies place great emphasis on not only the estimation but also the standard error estimates of underlying parameters derived from a temporal model. This allows inferences to be made about the signal estimates and resulting conclusions to be drawn about the underlying data. It can often be advantageous to interrogate temporal models after spatial transformation of the data into the wavelet domain. Wavelet bases provide a multiresolution decomposition of the spatial data dimension and an ensuing reduction in spatial correlation. However, widespread acceptance of these wavelet techniques has been hampered by the limited ability to reconstruct both parametric and error estimates into the image domain after analysis of temporal models in the wavelet domain. This paper introduces a derivation and a fast implementation of a method for the calculation of the variance of the parametric images obtained from wavelet filters. The technique is proposed for a class of estimators that have been shown to be useful in neuroimaging studies. The techniques are demonstrated for both functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) data sets.     

Temporal Filtering of Event-Related fMRI Data Using Cross-Validation

To circumvent the problem of low signal-to-noise ratio (SNR) in event-related fMRI data, the fMRI experiment is typically designed to consist of repeated presentations of the stimulus and measurements of the response, allowing for subsequent averaging of the resulting data. Due to factors such as time limitation, subject motion, habituation, and fatigue, practical constraints on the number of repetitions exist. Thus, filtering is commonly applied to further improve the SNR of the averaged data. Here, a time-varying filter based on theoretical work by Nowak is employed. This filter operates under the stationary wavelet transform framework and is demonstrated to lead to good estimates of the true signals in simulated data. The utility of the filter is also shown using experimental data obtained with a visual–motor paradigm.     

A method to produce evolving functional connectivity maps during the course of an fMRI experiment using wavelet-based time-varying Granger causality

Functional magnetic resonance imaging (fMRI) is widely used to identify neural correlates of cognitive tasks. However, the analysis of functional connectivity is crucial to understanding neural dynamics. Although many studies of cerebral circuitry have revealed adaptative behavior, which can change during the course of the experiment, most of contemporary connectivity studies are based on correlational analysis or structural equations analysis, assuming a time-invariant connectivity structure. In this paper, a novel method of continuous time-varying connectivity analysis is proposed, based on the wavelet expansion of functions and vector autoregressive model (wavelet dynamic vector autoregressive-DVAR). The model also allows identification of the direction of information flow between brain areas, extending the Granger causality concept to locally stationary processes. Simulation results show a good performance of this approach even using short time intervals. The application of this new approach is illustrated with fMRI data from a simple AB motor task experiment.     

Integrating EEG and fMRI in epilepsy

Integrating electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) studies enables to non-invasively investigate human brain function and to find the direct correlation of these two important measures of brain activity. Presurgical evaluation of patients with epilepsy is one of the areas where EEG and fMRI integration has considerable clinical relevance for localizing the brain regions generating interictal epileptiform activity. The conventional analysis of EEG-fMRI data is based on the visual identification of the interictal epileptiform discharges (IEDs) on scalp EEG. The convolution of these EEG events, represented as stick functions, with a model of the fMRI response, i.e. the hemodynamic response function, provides the regressor for general linear model (GLM) analysis of fMRI data. However, the conventional analysis is not automatic and suffers of some subjectivity in IEDs classification. Here, we present an easy-to-use and automatic approach for combined EEG-fMRI analysis able to improve IEDs identification based on Independent Component Analysis and wavelet analysis. EEG signal due to IED is reconstructed and its wavelet power is used as a regressor in GLM. The method was validated on simulated data and then applied on real data set consisting of 2 normal subjects and 5 patients with partial epilepsy. In all continuous EEG-fMRI recording sessions a good quality EEG was obtained allowing the detection of spontaneous IEDs and the analysis of the related BOLD activation. The main clinical finding in EEG-fMRI studies of patients with partial epilepsy is that focal interictal slow-wave activity was invariably associated with increased focal BOLD responses in a spatially related brain area. Our study extends current knowledge on epileptic foci localization and confirms previous reports suggesting that BOLD activation associated with slow activity might have a role in localizing the epileptogenic region even in the absence of clear interictal spikes.     

Multi-resolution Bayesian regression in PET dynamic studies using wavelets

In the kinetic analysis of dynamic PET data, one usually posits that the variation of the data through one dimension, time, can be described by a mathematical model encapsulating the relevant physiological features of the radioactive tracer. In this work, we posit that the remaining dimension, space, can also be modeled as a physiological feature, and we introduce this concept into a new computational procedure for the production of parametric maps. An organ and, in the instance considered here, the brain presents similarities in the physiological properties of its elements across scales: computationally, this similarity can be implemented in two stages. Firstly, a multi-scale decomposition of the dynamic frames is created through the wavelet transform. Secondly, kinetic analysis is performed in wavelet space and the kinetic parameters estimated at low resolution are used as priors to inform estimates at higher resolutions. Kinetic analysis in the above scheme is achieved by extension of the Patlak analysis through Bayesian linear regression that retains the simplicity and speed of the original procedure. Application to artificial and real data (FDG and FDOPA) demonstrates the ability of the procedure to reduce remarkably the variance of parametric maps (up to 4-fold reduction) without introducing sizeable bias. Significance of the methodology and extension of the procedure to other data (fMRI) and models are discussed.     

Wavelet-based fMRI analysis: 3-D denoising, signal separation, and validation metrics

We present a novel integrated wavelet-domain based framework (w-ICA) for 3-D denoising functional magnetic resonance imaging (fMRI) data followed by source separation analysis using independent component analysis (ICA) in the wavelet domain. We propose the idea of a 3-D wavelet-based multi-directional denoising scheme where each volume in a 4-D fMRI data set is sub-sampled using the axial, sagittal and coronal geometries to obtain three different slice-by-slice representations of the same data. The filtered intensity value of an arbitrary voxel is computed as an expected value of the denoised wavelet coefficients corresponding to the three viewing geometries for each sub-band. This results in a robust set of denoised wavelet coefficients for each voxel. Given the de-correlated nature of these denoised wavelet coefficients, it is possible to obtain more accurate source estimates using ICA in the wavelet domain. The contributions of this work can be realized as two modules: First, in the analysis module we combine a new 3-D wavelet denoising approach with signal separation properties of ICA in the wavelet domain. This step helps obtain an activation component that corresponds closely to the true underlying signal, which is maximally independent with respect to other components. Second, we propose and describe two novel shape metrics for post-ICA comparisons between activation regions obtained through different frameworks. We verified our method using simulated as well as real fMRI data and compared our results against the conventional scheme (Gaussian smoothing+spatial ICA: s-ICA). The results show significant improvements based on two important features: (1) preservation of shape of the activation region (shape metrics) and (2) receiver operating characteristic curves. It was observed that the proposed framework was able to preserve the actual activation shape in a consistent manner even for very high noise levels in addition to significant reduction in false positive voxels.     

Dual tree complex wavelet transform based denoising of optical microscopy images

Photon shot noise is the main noise source of optical microscopy images and can be modeled by a Poisson process. Several discrete wavelet transform based methods have been proposed in the literature for denoising images corrupted by Poisson noise. However, the discrete wavelet transform (DWT) has disadvantages such as shift variance, aliasing, and lack of directional selectivity. To overcome these problems, a dual tree complex wavelet transform is used in our proposed denoising algorithm. Our denoising algorithm is based on the assumption that for the Poisson noise case threshold values for wavelet coefficients can be estimated from the approximation coefficients. Our proposed method was compared with one of the state of the art denoising algorithms. Better results were obtained by using the proposed algorithm in terms of image quality metrics. Furthermore, the contrast enhancement effect of the proposed method on collagen fıber images is examined. Our method allows fast and efficient enhancement of images obtained under low light intensity conditions.OCIS codes: (100.0100) Image processing, (100.7410) Wavelets, (100.3020) Image reconstruction-restoration