Classification of EEG signals for epileptic seizure prediction using ANN

Kevric, Jasmin and Subasi, Abdulhamit (2012) Classification of EEG signals for epileptic seizure prediction using ANN. In: 3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo.

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In this paper, we developed a model for classification of EEG signals. The aim of the study is to determine whether this model can be used for epileptic seizure prediction if “pre-ictal” stages were successfully detected. We analyzed long-term Freiburg EEG data. Each of 21 patients contains datasets called “ictal” (seizure) and “inter-ictal” (seizure-free). We extracted 4096-samples (or 16 seconds) long segments from both datasets of each patient. These segments were decomposed into time-frequency representations using Discrete Wavelet Transform (DWT). The statistical features from the DWT sub-bands of EEG segments were calculated and fed as inputs to Multilayer Perceptron (MLP) and Radial Basis Function (RBF) network classifiers using 10-fold cross validation. We also applied multiscale PCA (MSPCA) de-noising method to determine if it can further enhance the classifiers’ performance. MLP-based approach outperformed RBF classifier with or without MSPCA, which significantly improved the classification accuracy of both classifiers. The proposed MLP-approach with MSPCAachieved a classification accuracy of 95.09%. We showed that a high classification accuracy of EEG signals can be accomplished in cases when additional “pre-ictal” class is introduced. Therefore, the proposed approach may become an efficient tool to predict epileptic seizures from EEG recordings. Keywords: Electroencephalogram (EEG); Epileptic seizure; Discrete Wavelet Transform (DWT); Multilayer Perceptron (MLP); Radial Basis Function (RBF) network; Multiscale PCA (MSPCA); Machine learning.

Item Type: Conference or Workshop Item (Paper)
Subjects: H Social Sciences > H Social Sciences (General)
T Technology > T Technology (General)
Divisions: Faculty of Economics > Management Department
Depositing User: Users 173 not found.
Date Deposited: 22 Oct 2012 09:01
Last Modified: 17 Dec 2012 15:19

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