Epilepsy disorder detection by EEG signal decomposition using wavelet transform

Electroencephalography is a medical imaging technique that reads scalp electrical activity generated by brain structures. The electroencephalogram (EEG) is defined as electrical activity of an alternating type recorded from the scalp surface after being picked up by metal electrodes and conductive media. We will refer only to EEG measured from the head surface. The recognition of epileptic waveform from EEG signal is important physiological task as epilepsy is still one of the most frequently occurring disorder. The main aim of this paper is to provide new method to diagnose the epileptic waveform directly from the EEG, by performing quick signal processing which makes it possible to apply in on-line monitoring system. This is done in two steps. In the first step, by using multi-resolution wavelet decomposition, we obtain different spectral components (α, β, Δ, θ) of the measured signal. These components serve as input signals for the artificial neural network (ANN), which accomplishes the recognition of epileptic waveforms. Use of ANN makes the rate of recognition very high and also makes the on-line monitoring and 'paperless' task of EEG analysis.