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MULTI-CHANNEL SIGNAL SEGMENTATION AND CLASSIFICATION

机译：多通道信号分割与分类

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摘要

Multi-channel sensors and multi-channel signal analysis form a specific area of general digital signal processing methods with applications in medicine, environmental signal analysis or technology. The paper is devoted to general mathematical methods related to initial signal de-noising, detection of its principal components and segmentation to find its specific parts. Feature detection includes the use of discrete wavelet transform (DWT) and discrete Fourier transform (DFT) for estimation of features invariant to signal shift to form clusters of close data segments. The self-organizing neural networks are then used for signal segments classification. Results are numerically evaluated by statistical analysis of distances of individual feature vector values from the corresponding cluster centers. Proposed methods are used for electroencephalogram (EEG) signal segmentation based upon detection of changes of signal spectral components applied to its first principal component, signal segments feature extraction and their classification. Results achieved are compared for different data sets and different mathematical methods used to detect signal segments features. Numerical results are compared with experience of experts specialized to EEG data analysis to allow further correlation with MR images. Proposed methods are accompanied by the appropriate graphical user interface (GUI) designed in the MATLAB environment.
机译：多通道传感器和多通道信号分析形成了通用数字信号处理方法的特定领域，并应用于医学，环境信号分析或技术领域。本文致力于与初始信号降噪，主要成分的检测以及分割以找到其特定部分有关的一般数学方法。特征检测包括使用离散小波变换（DWT）和离散傅里叶变换（DFT）来估计不随信号移位而形成紧密数据段簇的特征。然后将自组织神经网络用于信号段分类。通过对各个特征向量值与相应聚类中心的距离进行统计分析，对结果进行数值评估。提议的方法用于脑电图（EEG）信号分割，该方法基于检测应用于其第一主成分的信号频谱成分的变化，信号片段特征提取及其分类。比较了不同数据集和用于检测信号段特征的不同数学方法获得的结果。将数值结果与专门从事EEG数据分析的专家的经验进行比较，以进一步与MR图像相关。提出的方法随附有在MATLAB环境中设计的适当的图形用户界面（GUI）。

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