title: Dynamic principal component analysis with nonoverlapping moving window and its applications to epileptic EEG classification.
abstract: Classification of electroencephalography (EEG) is the most useful diagnostic and monitoring procedure for epilepsy study. A reliable algorithm that can be easily implemented is the key to this procedure. In this paper a novel signal feature extraction method based on dynamic principal component analysis and nonoverlapping moving window is proposed. Along with this new technique, two detection methods based on extracted sparse features are applied to deal with signal classification. The obtained results demonstrated that our proposed methodologies are able to differentiate EEGs from controls and interictal for epilepsy diagnosis and to separate EEGs from interictal and ictal for seizure detection. Our approach yields high classification accuracy for both single-channel short-term EEGs and multichannel long-term EEGs. The classification performance of the method is also compared with other state-of-the-art techniques on the same datasets and the effect of signal variability on the presented methods is also studied.
authors.affiliation: Department of Global Management Studies, Ted Rogers School of Management Studies, Ryerson University, 350 Victoria Street, Toronto, ON, Canada M5B 2K3.
authors.forename: Shengkun
authors.identifier: 0000-0002-9533-2096
authors.initials: S
authors.lastname: Xie
authors.affiliation: Department of Electrical and Computer Engineering, Ryerson University, 350 Victoria Street, Toronto, ON, Canada M5B 2K3.
authors.forename: Sridhar
authors.identifier: 0000-0002-4659-564X
authors.initials: S
authors.lastname: Krishnan
mesh_terms: D000465:Algorithms; D004569:Electroencephalography; D004827:Epilepsy / Q000175:diagnosis*; D006801:Humans; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012640:Seizures / Q000175:diagnosis