Denoising array-based comparative genomic hybridization data using wavelets

Array-based comparative genomic hybridization (array-CGH) provides a high-throughput, high-resolution method to measure relative changes in DNA copy number simultaneously at thousands of genomic loci. Typically, these measurements are reported and displayed linearly on chromosome maps, and gains and losses are detected as deviations from normal diploid cells. We propose that one may consider denoising the data to uncover the true copy number changes before drawing inferences on the patterns of aberrations in the samples. Nonparametric techniques are particularly suitable for data denoising as they do not impose a parametric model in finding structures in the data. In this paper, we employ wavelets to denoise the data as wavelets have sound theoretical properties and a fast computational algorithm, and are particularly well suited for handling the abrupt changes seen in array-CGH data. A simulation study shows that denoising data prior to testing can achieve greater power in detecting the aberrant spot than using the raw data without denoising. Finally, we illustrate the method on two array-CGH data sets.     

一个新的脑电信号分析系统：小波分析理论的运用

小波变换是一种把时间、频率（或尺度）两域结合起来的分析方法。它被誉为“分析信号的数学显微镜”。本系统将小波变换用于脑电信号分析,是一个在Ｗｉｎｄｏｗｓ３．１下开发的脑电分析系统。     

基于EMD和小波变换的运动分析

运动分析是视觉信息加工中的一个重要问题。本文利用Ｒｅｉｃｈａｒｄｔ的相关型初级运动检测器（ＥＭＤ）二维阵列可以有效地进行图象－背景相对运动分辨的特点,以及小波变换的频谱分析特性与人类视觉多频率通道特性相类似的性质,将ＥＭＤ模型、小波变换和图象的塔式结构处理有机地结合起来,提出了一种类似视觉信息加工方式的新的运动分析算法。计算机仿真结果表明,算法能够较好地模拟视觉运动检测的功能,与Ｈｏｒｎ＆Ｓｃｈｕｎｃｋ算法［１］相比,提高了运动估计的速度与精度。     

小波和主分量分析方法研究思维脑电

研究自发脑电和思维活动的关系．利用小波和主分量分析结合的WPCA算法对不同思维任务记录的六导脑电进行处理,并对思维特征的频谱能量和变化率等多指标进行综合分析和计算。结果表明WPCA算法不仅可以实现噪声的去除,而且能提高主分量的贡献率,降低输入矢量的维数。对脑电主分量的分析揭示了脑电与思维个体、思维种类、复杂度以及注意力的联系,思维任务的神经网络分类结果验证了WPCA方法研究脑电和思维的有效性,为进一步理解认知和思维过程,实现对思维的定位和分类提供了依据。     

诱发电位的多通道时频相干提取算法

多通道时频域相干成分提取算法是针对低信噪比的宽频带信号提取问题提出的。它采用多通道同步观测,在各通道的观测数据中信号成分具有较高的相干性,而噪声的相干性较低,因此根据其相干性的高低差别即可将信号与噪声分离,提取有效信号。为实现信号与噪声的分离,首先应用小波包分解将信号在时频域展开,然后通过计算相干系数确定信号的时频分布,最终通过小波包重构将信号从噪声中分离出来。这一算法不需要信号的任何先验知识,收敛快,可以有效地提取宽频带信号,极大地提高信号的信噪比,对非重复性信号具有良好的捕捉能力．应用此算法成功地实现了视觉诱发电位的单次提取。     

正常人体表心电图形态的逐拍变化

心电图的各种波形是众多心肌细胞动作电位在体表的综合效应。建立了对体表心电图的形态进行逐拍分析的硬件及软件系统。对５８例健康人体表心电图进行了采集和分析,得到了正常人体表心电图中Ｐ波,ＱＲＳ波,及Ｔ波的形态差异图,并进行了频谱及时域瞬态分析,从而展示了心肌电活动在体表心电图中的逐拍变化现象,体表心电图逐拍形态变化反映了心房肌和心室肌电活动的时变特征。     

虹膜识别中的一种神经算法的研究

研究了与传统身份识别不同的虹膜识别,在获取虹膜采样图后,经小波变换、松弛神经网络算法及稀疏编码,形成虹膜纹理的代码。并以海明距离作为决策空间的判决标准,实现人的虹膜图像识别的全过程,并对一些特定干扰条件下的虹膜图像进行计算机模拟试验,取得较好的结果。     

Multivariate classification of animal communication signals: A simulation-based comparison of alternative signal processing procedures using electric fishes

Evolutionary studies of communication can benefit from classification procedures that allow individual animals to be assigned to groups (e.g. species) on the basis of high-dimension data representing their signals. Prior to classification, signals are usually transformed by a signal processing procedure into structural features. Applications of these signal processing procedures to animal communication have been largely restricted to the manual or semi-automated identification of landmark features from graphical representations of signals. Nonetheless, theory predicts that automated time-frequency-based digital signal processing (DSP) procedures can represent signals more efficiently (using fewer features) than can landmark procedures or frequency-based DSP – allowing more accurate classification. Moreover, DSP procedures are objective in that they require little previous knowledge of signal diversity, and are relatively free from potentially ungrounded assumptions of cross-taxon homology. Using a model data set of electric organ discharge waveforms from five sympatric species of the electric fish Gymnotus, we adopted an exhaustive simulation approach to investigate the classificatory performance of different signal processing procedures. We considered a landmark procedure, a frequency-based DSP procedure (the fast Fourier transform), and two kinds of time-frequency-based DSP procedures (a short-time Fourier transform, and several implementations of the discrete wavelet transform -DWT). The features derived from each of these signal processing procedures were then subjected to dimension reduction procedures to separate those features which permit the most effective discrimination among groups of signalers. We considered four alternative dimension reduction methods. Finally, each combination of reduced data was submitted to classification by linear discriminant analysis. Our results support theoretical predictions that time-frequency DSP procedures (especially DWT) permit more efficient discrimination of groups. The performance of signal processing was found to depend largely upon the dimension reduction procedure employed, and upon the number of resulting features. Because the best combinations of procedures are dataset-dependent and difficult to predict, we conclude that simulations of the kind described here, or at least simplified versions of them, should be routinely executed before classification of animal signals - especially unfamiliar ones.     

Ultrasound signal wavelet analysis to quantify the microstructures of normal and frozen tissues in vitro

Cryosurgery has a number of advantages that make it particularly appealing in the treatment of liver cancer. However, a major problem for the wide clinical adoption of hepatic cryosurgery is the lack of a cost effective high resolution imaging way which is capable of both performing precise monitoring of the freezing process in situ and evaluating the postoperative effects after surgery. The mean scatterer spacing has been found to be an important parameter for describing the ultrasonic scattering and characterization of biological tissues. However, its potential values in the evaluation of cryosurgical effects of tissues reserved unclear so far. Here, we investigated the wavelet analysis to estimate the mean scatterer spacing parameter in normal and freeze–thawed tissues on porcine livers in vitro. The experimental results carried out at 10 MHz using weakly focused pulse-echo signal element transducer indicated that the mean scatterer spacing in normal liver tissues is 1.12 ± 0.13 mm whereas it is 1.67 ± 0.25 mm in several pre-frozen and then thawed tissues. These results disclosed the good correlation between the wavelet data and microstructures of the normal or thawed tissues, and hence demonstrated that the wavelet analysis holds promise to be used as an effective method for the characterization of thawed tissues scatterer spacing. The present method offers a potential pragmatic strategy for monitoring the transition zone between frozen and unfrozen tissues during the surgical therapy, and evaluating postoperative effects.