Epileptic Spike Detection with EEG using artificial Neural Networks

Epilepsy is a neurological disease that causes seizures in its victims that can lead to physical injury or even death in some circumstances. It is caused by excessive, synchronous abnormal firing of neurons in the brain. This chronic disease has no known cure and affects millions of people worldwide but can be managed through various methods. The successful treatment is dependent upon correct identification of the origin of the seizures within a brain. One major challenge for doctors is the analysis of the immense amount of data collected by electroencephalogram (EEG) devices. In order to identify a region of the brain that causes epileptic seizures, millions of samples must be analyzed manually by a trained eye to find interictal spikes that emanate from the afflicted region of the brain. This paper presents a method for automatic interictal spike detection while minimizing false positives. In this way, it eliminates the lengthy, manual process currently used by doctors. Analyzing real world data, the presented Neural Network Epileptic Spike Detector (NNESD) showed a PPV of 72.67% and sensitivity of 82.68% on average over 300 trained networks on a single channel of EEG.