Reduction of massive EEG datasets for epilepsy analysis using Artificial Neural Networks

Abstract

Epileptic seizure source identification involves neurologists combing through a substantial amount of data manually, which sometimes takes weeks per patient. This paper presents a methodology for minimizing the amount of data a neurologist has to analyze to identify the seizure focus. The method keeps the neurologist as the final decision maker and aids in the decision making process. It has to be noted that the primary focus of the work was not improving the accuracy of interictal spike detection but reduction of the volume of data. The presented methodology is based on Artificial Neural Networks (ANN) and is implemented on EEG data collected on 5 patients using a dense array EEG reader. As a baseline, a simple template matching was implemented on the same dataset. Experimental results showed that the ANN based methodology was able to reduce the dataset by 98%, a significant improvement on the template matching method.