Abstract
Epilepsy is a chronic disease caused by sudden abnormal discharges of neurons which lead to transient brain disfunction. For those patients who need surgery, the recognition of the epileptic foci is one of the most important steps. In this paper, a combination of EEG signals processed by Short-time Fourier Transform and processed by Continuous Wavelet Transform is introduced. A deep learning model with an accuracy of 91.3% trained by Convolution Neural Network has been obtained. The experiment uses a methodology that has not been widely tested so far, and it also demonstrated the practical values by its relatively small time consumption and computing power requirements meanwhile ensuring high accuracy. Furthermore, it proves the feasibility of improving accuracy by combining different feature extraction methods.
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