噪声中EP信号的提取算法及研究进展

诱发电位的提取是脑电信号处理领域的前沿课题近年来 ,通过少次甚至单次试验提取诱发电位已经成为研究的主流。本文对近年来提取诱发电位的信号处理方法进行了简要的回顾 ,并分别从小波变换、神经网络、高阶累积量、独立分量分析等四个方面对算法进行了介绍     

Study on Singularity of Chaotic Signal Based on Wavelet Transform

INTRODUCTION The significant information of a signal is often carried by singular characteristics or irregular struc-tures of the signal, for example, the most important information of ECG(electrocardiogram) or EEG isoften presented at the transient points of a signal, such as those points near peaks. The singular charac-teristics of these transient points are more obvious than the smooth parts of signals. Therefore,to studythe singularity of a signal is a meaningful work. Those analysi…     

Medical data analysis using microprocessor-based Walsh transformations

The use of Fourier transforms based on orthogonal sinusoidal functions is well established in the analysis of spectral components in noisy medical signals. In this paper the use of Walsh transformations based on orthogonal discrete signals is described for the analysis of ‘slow-wave’ rhythms in the gastro-intestinal tract of mammals. The Walsh transform approach has the advantage that very fast algorithms can be developed since multiplications involving sinusoidal functions are replaced by additions and subtractions. These algorithms are very suitable for simple microprocessors and it is shown that, for determining the frequency of gut rhythms, simple filtering and signal conditioning preceding the microprocessor dispenses with the need for analogue to digital conversion. Hence a very simple digital microprocessing system for the continuous monitoring of gut rhythms has been produced. It has also been extended to provide filtering of signals via forward and inverse Walsh transforms.     

基于多分辨分析与连续小波变换提取和分析兔体感诱发电位

研究单次提取兔体感诱发电位,并定位和分析诱发电位波形成分。麻醉兔,以0.5Hz频率电脉冲刺激兔下肢隐神经,3764Hz采样率收集兔头皮电位。采用一维多分辨分析提取兔体感诱发电位,并用连续小波变换定位和分析诱发电位波形成分。单次诱发电位的小波变换与叠加平均诱发电位比较,表明Daubechies小波多分辨分析可以单次提取诱发电位。连续小波变换能够精确定位诱发电位中波形成分,并可采用连续小波变换分析诱发成分的频域特性。连续小波变换技术把一维时域信号投影到二维时频空间研究将成为医学信号处理的一个有用方法。     

改进的小波域医学图像序列的运动估计

医学图像序列压缩是远程医疗系统中的重要技术，而运动估计在视频序列压缩中起着关键作用。我们提出了一种改进的正方形-菱形搜索算法来实现医学图像序列的运动估计。这种改进的正方形-菱形算法减少了搜索点数。我们将其应用于小波域的医学图像序列的运动估计，并对数字减影血管造影图像序列（DSA）进行实验。结果表明，改进后的小波域正方形-菱形算法较其他算法精度高。     

眼底图像畸变的校正及多幅图像的特征点拼接算法

对于眼底图像 ,由于不在正视点取像造成的几何畸变和由于荧光素从血管壁渗漏等造成的灰度畸变 ,几乎是不可避免的。为临床诊断提供丰富信息的需要出发 ,进行多幅眼底图像的拼接是必要的。本文为此提出了几个新的处理方法 ,如基于大窗口的滤波灰度校正方法、基于透视投影原理的几何校正方法与基于小波子空间上的拼接算法 ,均具有效果好和耗时少的优点。     

An intelligent system for diagnosis of the heart valve diseases with wavelet packet neural networks

In this paper, an intelligent system is presented for interpretation of the Doppler signals of the heart valve diseases based on the pattern recognition. This paper especially deals with combination of the feature extraction and classification from measured Doppler signal waveforms at the heart valve using the Doppler Ultrasound. Because of this, a wavelet packet neural network model developed by us is used. The model consists of two layers: wavelet and multi-layer perceptron. The wavelet layer is used for adaptive feature extraction in the time-frequency domain and is composed of wavelet packet decomposition and wavelet packet entropy. The multi-layer perceptron used for classification is a feed-forward neural network. The performance of the developed system has been evaluated in 215 samples. The test results showed that this system was effective in detecting Doppler heart sounds. The correct classification rate was about 94% for abnormal and normal subjects.     

Signal-dependent wavelets for electromyogram classification

In the study, an efficient method to perform supervised classification of surface electromyogram (EMG) signals is proposed. The method is based on the choice of a relevant representation space and its optimisation with respect to a training set. As EMG signals are the summation of compact-support waveforms (the motor unit action potentials), a natural tool for their representation is the discrete dyadic wavelet transform. The feature space was thus built from the marginals of a discrete wavelet decomposition. The mother wavelet was designed to minimise the probability of classification error estimated on the learning set (supervised classification). As a representative example, the method was applied to simulate surface EMG signals generated by motor units with different degrees of short-term synchronisation. The proposed approach was able to distinguish surface EMG signals with degrees of synchronisation that differed by 10%, with a misclassification rate of 8%. The performance of a spectral-based classification (error rate approximately 33%) and of the classification with Daubechies wavelet (21%) was significantly poorer than with the proposed wavelet optimisation. The method can be used for a number of different application fields of surface EMG classification, as the feature space is adapted to the characteristics of the signal that discriminate between classes. An erratum to this article is available at .     

采用小波描述子实现表面渲染数据的渐进传输

为了实现医学可视化的网络应用,提出一种基于小波描述子的表面渲染数据渐进传输方法.首先针对用于表面重建的序列平面轮廓为周期序列的特点,提出数字轮廓小波描述子的概念.基于小波描述子进行渐进传输的基本思想是,在发送端将轮廓采用小波描述子表示,然后对这些小波描述子系数序列渐进发送.在接收端,利用陆续收到的描述子系数序列重构轮廓,进行表面重建与渲染,重建精度和渲染效果随着不断收到‘细节'部分数据而得到改善并最终达到发送端的效果.该方法可以在数据无损传输的情况下,降低对传输网络带宽的要求,文中实验研究表明了方法的可行性.     

儿童失神癫(癎)脑电图的多尺度特征

目的:研究儿童失神癫癎脑电图的多尺度定量特征。方法:对15例失神癫癎患儿10次临床发作和20次亚临床癎样放电的脑电图进行子波分析,提取失神癫癎发作过程中脑电信号的多尺度定量典型特征,与发作前10 s及发作后10 s的脑电信号进行比较,并与12例正常同龄儿童脑电图进行比较。结果:研究显示儿童失神癫癎发作过程中脑电信号的多尺度典型特征主要表现为12尺度(对应频率3 Hz)的节律性活动显著增强,发作时20尺度(低频大尺度,对应频率0．12 Hz)结构与频率3 Hz的结构具有非正常的跳跃式尺度关系,3 Hz节律性棘慢复合波与大尺度(频率1 Hz以下)背景低频放电结构共同存在。发作过程中分尺度功率主要集中在20尺度和12尺度,其演变规律为20尺度能量逐渐减低,12尺度能量逐渐增加。10次临床发作的脑电信号均显示上述特征。发作前10 s和后10 s的脑电多尺度信号中仍然存在隐性的3 Hz棘慢复合波成分,与一般认为3 Hz棘慢复合波突起突止不同．而从传统的脑电图上无法分辨出发作前后的这些多尺度细节的定量特征。亚临床癎样放电的多尺度特征与发作期无明显差别,但持续时间短。结论:子波分析作为一种新的信号分析方法,适合于脑电信号的分析,可以获得比传统视觉脑电图更多的定量信息。通过对失神癫癎患儿的脑电信号进行子波分析,得到其发作过程中典型的多尺度定量特征,有助于失神癫癎发作的临床辅助诊断、预后评价以及神经电生理机理的基础研究。