title: On the proper selection of preictal period for seizure prediction.
abstract: 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 a more accurate classifier model. The proposed method uses amplitude distribution histograms of features extracted from electroencephalogram (EEG) recordings. To evaluate this method, we extract spectral power features in different frequency bands from monopolar and space-differential EEG signals of 18 patients suffering from pharmacoresistant epilepsy. Furthermore, comparisons among monopolar channels with space-differential channels, as well as intracranial EEG (iEEG) and surface EEG (sEEG) signals, indicate that while monopolar signals perform better in iEEG recordings, no significant difference is noticeable in sEEG recordings.
authors.affiliation: CISUC/DEI, Center for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Polo II, 3030-290 Coimbra, Portugal. Electronic address: m.bandarabadi@gmail.com.
authors.forename: Mojtaba
authors.initials: M
authors.lastname: Bandarabadi
authors.affiliation: Department of Biomedical Engineering, Faculty of Engineering, Babol Noshirvani University of Technology, Babol, Iran.
authors.forename: Jalil
authors.initials: J
authors.lastname: Rasekhi
authors.affiliation: CISUC/DEI, Center for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Polo II, 3030-290 Coimbra, Portugal.
authors.forename: César A
authors.initials: CA
authors.lastname: Teixeira
authors.affiliation: Department of Biomedical Engineering, Faculty of Engineering, Babol Noshirvani University of Technology, Babol, Iran.
authors.forename: Mohammad R
authors.initials: MR
authors.lastname: Karami
authors.affiliation: CISUC/DEI, Center for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Polo II, 3030-290 Coimbra, Portugal.
authors.forename: António
authors.initials: A
authors.lastname: Dourado
mesh_terms: D000293:Adolescent; D000328:Adult; D002648:Child; D004569:Electroencephalography / Q000592:standards / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D000069550:Machine Learning / Q000706:statistics & numerical data*; D008297:Male; D008875:Middle Aged; D012640:Seizures / Q000175:diagnosis*; D013997:Time Factors; D055815:Young Adult
keywords: Amplitude distribution histogram; Epilepsy; Machine learning; Preictal period; Seizure prediction