Title:Classification of Normal and Epileptic EEG Signals Using Adaptive Neuro-Fuzzy Network Based on Time Series Prediction
VOLUME: 4 ISSUE: 4
Author(s):Hossein Komijani*, Armin Nabaei and Houman Zarrabi
Affiliation:Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Department of Computer Engineering, Islamic Azad University, Science and Research Branch, Tehran, ICT Research Institute, Tehran
Keywords:Classification, electroencephalography (EEG), epileptic, adaptive neuro-fuzzy network (ANFIS).
Abstract:Background: Epilepsy is one of the brain illnesses known as epileptic seizures which occurs because
of the disruption of the electrical communication between neurons. Epilepsy causes abnormalities in
electroencephalogram (EEG) signals. Therefore, with observing and declaring the relevant abnormalities in
EEG signals, the Epilepsy would be recognized.
Objective: This paper presents a novel classification approach for normal and epileptic electroencephalogram
(EEG) signals recorded from healthy and epilepsy persons.
Methods: The classification approach is based on time series prediction. Two adaptive neuro-fuzzy networks
(ANFIS) are trained to predict one-step-ahead for the EEG time-series data, where one ANFIS is trained on
EEG signals of a healthy person and the other on EEG signals of epilepsy. Classification is performed from
a window through which all predicted signals were passed.
Results: Separability of classes is obtained because of the morphological dissimilarity of the EEG signals in
diverse classes, and each ANFIS specializes in the sort of EEG-data on which it is trained with. This approach
is performed on thirteen subjects’ EEG signals with classification accuracy rate of about 98%.
Conclusion: The classification is performed in the time domain, and there is no need to map the signals to
the other prevalent domain such as the frequency domain (like wavelet transform). Besides, the classification
does not need any pre-processing on the EEG signals such as feature extraction and dimension reduction.
