An automated classification of EEG signals based on spectrogram and CNN for epilepsy diagnosis

Epilepsy disease is one of the most prevalent neurological disorders caused by malfunction of large symptoms number of neurons. That's lead us to propose an automated approach to classify Electroencephalography (EEG) signals of the aforementioned pathology. To realize an efficient seizures detection the output of our classification is divided into three classes; normal, pre-ictal and ictal class. In fact, we propose to use the Short-Time Fourier Transform (STFT) as a non-stationary signal processing technique to extract useful information from the EEG signals. After that, we transform the STFT into a spectrogram image which will be used as an input in the classification process. In this context, we developed a deep convolutional neural network (CNN) model capable to efficiently detect and classify epilepsy seizures based on the EEG spectrogram images. It should be noted that the database used in this work is the publicly available EEG data set of Bonn University. In order to evaluate the performance of the proposed classification method several metrics are calculated, such as; sensitivity, specificity, accuracy and precision. The experimental results prove that the proposed method is a powerful tool in classifying EEG signals with a high average accuracy rate of 98.22%.