Multi-method synthesizing to detect and classify epileptic waves in EEG

A synthesized multi-method is introduced to detect and classify the epileptic waves in the EEG data. By this method, several signal processing methods, such as wavelet transformation (WT), artificial neural networks (ANN) and expert rules (ER) were synthesized in order to exploit the advantages of different methods sufficiently. At first, the epileptic waves were detected from pre-processed EEG data at different scales by WT, after then the characteristic parameters of the chosen candidates of epileptic waves were extracted and sent into the well-trained ANN to identify and classify the true epileptic waves. At last, the detected epileptic waves were certificated by ER. The statistic results of detection and classification show that, the synthesized multi-method has a good capacity to extract signal features and to shield the signals from the random noise. This method is especially fit for the analysis of the biomedical signals in biomedical engineering, which are usually non-placid and non-linear.