Supervised machine learning-based seizure prediction methods consider preictal period as
an important prerequisite parameter during training. However, the exact length of the preictal
state is unclear and varies from seizure to seizure. We propose a novel statistical approach
for proper selection of the preictal period, which can also be considered either as a measure
of predictability of a seizure or as the prediction capability of an understudy feature. The
optimal preictal periods (OPPs) obtained from the training samples can be used for building …