On the classification of EEG signal by using an SVM based algorithm

Abstract

In clinical practice, study of brain functions is fundamental to notice several diseases potentially dangerous for the health of the subject. Electroencephalography (EEG) can be used to detect cerebral disorders but EEG study is often difficult to implement, taking into account themultivariate and non-stationary nature of the signals and the invariable presence of noise. In the field of Signal Processing exist many algorithms and methods to analyze and classify signals reducing and extracting useful information. Support Vector Machine (SVM) based algorithms can be used as classification tool and allow to obtain an efficient discrimination between different pathology and to support physicians while studying patients. In this paper, we report an experience on designing and using an SVM based algorithm to study and classify EEG signals. We focus on Creutzfeldt-Jakob disease (CJD) EEG signals. To reduce the dimensionality of the dataset, principal component analysis (PCA) is used. These vectors are used as inputs for the SVM classifier with two classification classes: pathologic or healthy. The classification accuracy reaches 96.67% and a validation test has been performed, using unclassified EEG data.