Epileptic seizure prediction using machine learning and deep learning methods

Epilepsy is one of the most common neurological diseases in the world which negatively affects the daily life of a patient. Predicting epileptic seizures is of great importance for healthcare professionals and patients. The electroencephalography (EEG), which allows for registering brain activity with the help of electrodes placed on the scalp, is generally used to diagnose and monitor epilepsy. In this study, automatic seizure prediction was performed using CHB-MIT dataset which contains EEG data recorded at Boston Children's Hospital. Support Vector Machines (SVM), a common machine learning algorithm chosen as the primary method within this thesis's scope, and three different deep learning methods were compared. The first of these methods was long short term memory (LSTM) classifier with convolutional autoencoder which did not need any feature extraction. The second method used the spectrograms obtained by preprocessing the EEG data which were fed into a convolutional neural network (CNN) based classifier. The last method was based on converting the EEG data into three-dimensional images by applying source localization and performing classification with CNN. Among the methods used, the best result was obtained using source localization based CNN classification with 89.06% specificity, 92.58% sensitivity and 90.41% accuracy. Computational cost of three methods in terms of runtime efficiency were also compared, and it was observed that the SVM, which yielded the lowest classification performance with 74.07% accuracy, worked significantly faster than other methods.