Epileptic seizure prediction by exploiting spatiotemporal relationship of EEG signals using phase correlation

Mohammad Zaved Parvez, Manoranjan Paul

Research output: Contribution to journalArticlepeer-review

54 Citations (Scopus)
5 Downloads (Pure)

Abstract

Automated seizure prediction has a potential in epilepsy monitoring, diagnosis, and rehabilitation. Electroencephalogram (EEG) is widely used for seizure detection and prediction. This paper proposes a new seizure prediction approach based on spatiotemporal relationship of EEG signals using phase correlation. This measures the relative change between current and reference vectors of EEG signals which can be used to identify preictal/ictal (before the actual seizure onset/ actual seizure period) and interictal (period between adjacent seizures) EEG signals to predict the seizure. The experiments show that the proposed method is less sensitive to artifacts and provides higher prediction accuracy (i.e., 91.95%) and lower number of false alarms compared to the state-of-the-art methods using intracranial EEG signals in different brain locations of 21 patients from a benchmark data set.
Original languageEnglish
Pages (from-to)158-168
Number of pages11
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume24
Issue number1
Early online date2015
DOIs
Publication statusPublished - Jan 2016

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