In this thesis, I focus on exploiting electroencephalography (EEG) signals for early seizure diagnosis in patients. This process is based on a powerful deep learning algorithm for times series data called Long Short-Term Memory (LSTM) network. Since manual and visual inspection (detection) of epileptic seizure through the electroencephalography (EEG) signal by expert neurologists is time-consuming, work-intensive and error-prone and it might take a couple hours for experts to analyze a single patient record and to do recognition when immediate action is needed to be taken. This thesis proposes a reliable automatic seizure/non-seizure classification method that could facilitate the identification process of characteristic epileptic patterns, such as pre-ictal spikes, seizures and determination of seizure frequency, seizure type, etc. In order to recognize epileptic seizure accurately, the proposed model exploits the temporal dependencies in the EEG data. Experiments on clinical data present that this method achieves a high seizure prediction accuracy and maintains reliable performance. This thesis also finds the most efficient lengths of EEG recording for highest accuracies of different classification in the automated seizure detection realm. It could help non-experts to predict the seizure more comprehensively and bring awareness to patients and caregivers of upcoming seizures, enhancing the daily lives of patients against unpredictable occurrence of seizures.
Immediate visual memory as a function of epileptic seizure type