Focal epileptic seizure forecasting in EEG signals using wavelet transform and artificial neural networks

Seizure prediction has become a major field of neurological research, because of the suggestion of recent research that electrophysiological changes develop minutes to hours, before the actual clinical onset in focal epileptic seizures. This paper describes a novel approach for forecasting focal epileptic seizures, by applying statistical analysis methods and classifying, the features derived from intracranial Electroencephalographic (EEG) recordings, of brain activity. 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 nonstationary signals, such as EEGs. The performance of the ANN classifier is evaluated in terms of sensitivity, specificity and classification accuracy. The obtained classification accuracy confirms that the proposed scheme has potential in classifying EEG signals.