Pre-Ictal phase detection algorithm based on one dimensional EEG signals and two dimensional formed images analysis

Abstract

Over 50 million persons worldwide are affected by epilepsy. It can affect equally young babies as well as old people. Epilepsy is a brain disorder characterized by the neurobiological, cognitive, psychological and social consequences. It is known for sudden, unexpected transitions from normal to pathological behavioral states called epileptic seizures. Epilepsy poses a significant burden to society due to associated healthcare cost to treat and control the unpredictable and spontaneous occurrence of seizures. There is a need for a quick screening process that could help neurologists diagnose and determine the patient's treatment.Electroencephalogram has been traditionally used to diagnose patients by evaluating those brain functions that might correspond to epilepsy. This research focuses on developing new classification technique and the prediction of pre-ictal states that announce epileptic seizures, from the online EEG data analysis. The idea is to place electrodes on critical regions on the patient's head that would wirelessly communicate with the EEG recorder and the unit that performs online automated pre-ictal state detection based on obtained EEG signal. The patient should get timely alert about the possible seizure attack so that she/he can stop with its activities and take safety precautions.

Comparison of Different Machine Learning Algorithms to Classify Epilepsy Seizure from EEG Signals

“…The EEG records the continuous electrical impulses in the form of signals, and it records the abnormal electrical activity that leads to seizures. Therefore, the first step is always to find the correct dataset [9]. In this paper, we studied various epileptic seizure databases that were released by various research institutes around the world.…”