Study On Feature Extraction And Classification Of Melancholia EEG

Melancholia is a kind of common dysfunction disease characterized by an obvious reduction in intellectual and physiological vigor. Some related studies found that electroencephalogram (EEG) signals of melancholic differ from that of healthy persons in rhythm, wave amplitude and power spectrum amplitude. A number of frequency components are included in spontaneous EEG and the energy corresponding to different frequency bands, which is detected in different physiological states and pathogeny, changes with the scalp area. Thus the energy corresponding to a certain frequency sub-band can be taken as a feature parameter of the classifier to realize the classification of melancholia EEG. In this paper, the use of wavelet analysis and eigenvector estimation was proposed for the extraction of discriminating features from melancholia and nomal people's EEG. Subsequently, we achieved the classification combining with SVM. The main work done in this dissertation is as follows:1. The EEG recordings were decomposed into various frequency bands through multiscale decomposition by the method of wavelet transaction (WT) and wavelet package transaction (WPT) respectively. And then, we extracted the energy feature using wavelet coefficients. WT is a multiscale signal analysis method which key feature is the time -freqency localsation, and it is suitable for capturing transient nature of nonstationary EEG signals. With the trait of arbitrary multiscale decomposition, WPT cover the shortage of fixed time-frequency decomposition in WT (i.e. poor frequency resolution for high frequency component). Therefore, WPT has better time-frequency charactristic and provides more choice in time-frequency signal analysis.2. By applying the method of eigenvector estimation to the feature extraction of EEG signal, we carry a statistical analysis on the EEG power spectrum amplitude. And then, we take the maximum, minimum, mean and standard deviation of EEG power spectrum amplitude as characteristic parameters. Eigenvector estimation is a non-parametric method based on an eigen-decomposition of the correlation matrix of the noise-corrupted signals. It is best suited to the signals assumed to be composed of several specific sinusoids buried in noise. Even when the signal-to-noise ratio (SNR) is low, the eigenvector estimation can still obtain a high resolution of frequency spectra.3. Having finished the feature extraction of the melancholic and healthy persons' EEG, we achieved the classification of these two kinds of EEG by Support Vector Machine (SVM). SVM is a machine learning method based on statistics theory, which includes a number of techniques such as the largest interval hyper plane, Mercer kernel, convex quadratic programming and relaxation variables etc. According to the principle of minimizing structural risk, SVM enhances the generalization capability of learning machine and converts the optimization problem into a convex quadratic programming problem. Since the solution of this convex quadratic programming problem is unique and global, local extremum problem existing in general neural networks doesn't occur. Since practical problems such as nonlinear problem, high dimension problem and local extremum problem have been resolved, SVM obtains the best performance in a variety of practical applications with much challenge. Consequently, SVM has gradually become a superior tool to solve the problem of pattern classification.Experiments demonstrate that taking the feature parameter, which is extracted by the above three feature extraction methods, as the input eigenvector can achieve ideal clsssification accuracy which arrives 87%. This paper presented a new method for melancholia diagnose. The present research provides a basis for the ongoing study "research of melancholic diagnose based on spontaneous EEG".