A channel selection method for epilepsy seizure prediction

Abstract

The development of systems that can predict epilepsy seizures in real time offers great hope for epilepsy patients. These systems aim to prevent accidents that patients may experience due to loss of consciousness during seizures. Therefore, systems that can predict epileptic seizures should both work in real time and be designed to maintain the daily activities of the patient. In this case, a system with as few electrodes as possible should be developed. In this study, it is aimed to choose the most appropriate electrode in predicting epileptic seizures. Channel selection is made according to two parameters and its effect on seizure prediction is examined. The first parameter is the difference in variance between preictal and interictal; The other parameter is the weighted average sensitivity (WAS). The Rusboosted Tree ensemble classification is used to calculate WAS. The prediction process is carried out with the method we proposed in the previous study. For performance evaluation, prediction accuracy, sensitivity (SEN) and false alarm rates per hour (FPR) are calculated. The prediction performance for the channel selected according to the variance difference results are 69%, 70.9% and 0.054 respectively and the for the channel selected according to WAS results are 69%, 71.8% and 0.031 respectively. © 2021 IEEE.