Epilepsy Detection through Electroencephalogram Analysis using Artificial Intelligence

Abstract

Epilepsy is one of the most common neurological diseases, characterized by recurrent attacks of abnormal brain activity. The electroencephalogram (EEG) is an essential tool for diagnosing and monitoring epilepsy, as it records the electrical activity of the brain. This research project proposes to study the patterns of brain electrical activity associated with different types of epilepsy and develop signal processing algorithms for automatic detection of epileptiform activity in EEGs.The aim is to explore machine learning techniques, such as artificial neural networks (ANN) and support vector machines (SVM) to predict epileptic seizures based on EEG patterns. The preventive detection of an epileptic seizure could be used as a trigger for treatments such as neuromodulation, which uses the application of electrical signals in order to modulate the functioning of the brain and, thus, stop the seizure.