A Novel Approach for EEG Signal Classification using Wavelet Transform and Random Forests

Unprovoked seizure is the symptoms of Epilepsy disorder. An electroencephalogram (EEG) is a test that perceives electrical activity in your mind using nearly nothing, level metal circles (anodes) added to your scalp. The EEG signals are used for diagnosis of the patient whether it is seizure or non-seizure which causes epilepsy. The essential objective of this paper is to build a classifier that can accurately recognize whether a subject is seizure patient or nonseizure patient. The proposed system classifies the EEG signals into two classes using different supervised learning algorithm. Features are extracted using discrete wavelet transform (DWT) and Genetic algorithm approach. Features dimensions have been reduced using Principal component analysis (PCA) before feature extraction. Different classification algorithms like support vector machine, logistic regression and decision tree classifier. Random forest based classification with regularization gives best results which is best from the existing system