Research Area:  Machine Learning

Abstract:

In this work, we proposed a versatile signal processing and analysis framework for Electroencephalogram (EEG). Within this framework the signals were decomposed into the frequency sub-bands using DWT and a set of statistical features was extracted from the sub-bands to represent the distribution of wavelet coefficients. Principal components analysis (PCA), independent components analysis (ICA) and linear discriminant analysis (LDA) is used to reduce the dimension of data. Then these features were used as an input to a support vector machine (SVM) with two discrete outputs: epileptic seizure or not. The performance of classification process due to different methods is presented and compared to show the excellent of classification process. These findings are presented as an example of a method for training, and testing a seizure prediction method on data from individual petit mal epileptic patients. Given the heterogeneity of epilepsy, it is likely that methods of this type will be required to configure intelligent devices for treating epilepsy to each individuals neurophysiology prior to clinical operation.

Keywords:  

Author(s) Name:  Abdulhamit Subasi, M. Ismail Gursoy

Publisher name:  Elsevier

DOI:  10.1016/j.eswa.2010.06.065

Volume Information:  Volume 37, Issue 12, December 2010, Pages 8659-8666

Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S0957417410005695