Multi-Channel Signal Segmentation and Classification

A. Prochzka, V. Bartoov, and O. Vyata (Czech Republic)


Multi-channel signal proceesing, discrete wavelet trans form, principal component analysis, feature extraction, biomedical signal and image analysis


Multi-channel sensors and multi-channel signal analysis form a specific area of general digital signal processing methods with applications in medicine, environmental sig nal 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 sig nal shift to form clusters of close data segments. The self organizing neural networks are then used for signal seg ments 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 compo nent, signal segments feature extraction and their classifica tion. 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 meth ods are accompanied by the appropriate graphical user in terface (GUI) designed in the MATLAB environment.

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