Forecasting epileptic seizures using EEG signals, wavelet transform and artificial neural networks

Abstract

Electroencephalograms (EEG) are signal records of electrical activity of brain neurons. EEG, which is a compulsive tool/used for diagnosing neurological diseases such as epilepsy, besides of techniques such as magnetic resonance and brain tomography (BT) that are used for diagnosing structural brain disorders. This paper describes a novel approach for forecasting epileptic seizure activity, by classifying these EEG signals. The decision making consists of two stages; initially the signal features are extracted by applying wavelet transform (WT) and then an artificial neural network (ANN) model, which is a supervised learning-based algorithm classifier, used for signal classification. Wavelet transform is an effective tool for analysis of transient events in non-stationary signals, such as EEGs. The performance of the ANN classifier is evaluated in terms of sensitivity, specificity and classification accuracy. The obtained classification