Dynamic Principal Component Analysis with Nonoverlapping Moving Window and Its Applications to Epileptic EEG Classification
Joint Authors
Krishnan, Sridhar
Xie, Shengkun
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-01-16
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Xie, Shengkun& Krishnan, Sridhar. 2014. Dynamic Principal Component Analysis with Nonoverlapping Moving Window and Its Applications to Epileptic EEG Classification. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1049560
Modern Language Association (MLA)
Xie, Shengkun& Krishnan, Sridhar. Dynamic Principal Component Analysis with Nonoverlapping Moving Window and Its Applications to Epileptic EEG Classification. The Scientific World Journal No. 2014 (2014), pp.1-10.
https://search.emarefa.net/detail/BIM-1049560
American Medical Association (AMA)
Xie, Shengkun& Krishnan, Sridhar. Dynamic Principal Component Analysis with Nonoverlapping Moving Window and Its Applications to Epileptic EEG Classification. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1049560
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1049560