Classification of EEG Signals Using Wavelet Transform and Hybrid Classifier for Parkinson’s Disease Detection

---------------------------------------------------------Abstract------------------------------------------------------Feature extract ion and classification of Electroencephalograph (EEG) signals for normal and abnormal person is a challenge for engineers and scientists. Various signals processing techniques have already been proposed for classification of non-linear and non-stationary signals like EEG. In this work, Support Vector Machine (SVM) and Multilayerperceptron (MLP) based classifier was employed to detect Parkinson’s disease from background electroencephalograph signals. Signals where preprocessed, decomposed by using discrete wavelet transform (DWT) till 5 th level of decomposition tree. The proposed clas sifier may show the promising classification accuracy.