Human epileptic seizure prediction using deep convolutional neural network - Arenas, Angelo V.; Cosme, Jefferson G. (Quezon City College of Engineering, University of the Philippines Diliman, 2017)
Epilepsy is one of the most common neurological disorders characterized by recurrent and intermittent seizures Researchers have long studied how to detect and predict these seizures and machine learning techniques proved to be the most effective.

Over the past few years, different deep learning methods have appeared and no- table ones, such as the Convolutional Neural Network (ConvNet/CNN), have started to gain more attention in the machine learning field. Deep learning models are able to learn features at high levels of abstraction, making them really powerful classifiers ConvNets are specially good at learning features from images, and can take advantage of problems that are traditionally dealt with through visual inspection.

2-second segments of intracranial EEG data are converted into RGB images, which are then fed to the classifier for training and testing. The classifier is the GoogleNet Inception v3 that learns and detects patterns that discriminate pre-ictal and inter- ictal states.

As observed from the results, the model performed relatively well on the patient data scoring an accuracy of 89% and 85% with an AUC of 0.9519 and 0.9175 for patient I and patient 2, respectively. In terms of the running time, the system is feasible to use in real-time prediction as it can process and classify 2 second segments in just 0.07546 seconds on average.

Knowing the location of each channel can provide more information from the data. which can further improve the accuracy. An entirely different model, such as LSTM and 3D CNN, may also be more appropriate for classification since these models can take advantage of the time series nature of EEG data.
With user manual (bound with the main text).
Thesis (B.S. Computer Science)--University of the Philippines Diliman
Keywords: Epilepsy; Convulsions -- Forecasting; Neural networks (Computer science)