Epileptic Seizure Prediction Using CSP and LDA for Scalp EEG Signals
Joint Authors
Alshebeili, Saleh
Alotaiby, Turky N.
Alotaibi, Faisal M.
Alrshoud, Saud R.
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-10-31
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
This paper presents a patient-specific epileptic seizure predication method relying on the common spatial pattern- (CSP-) based feature extraction of scalp electroencephalogram (sEEG) signals.
Multichannel EEG signals are traced and segmented into overlapping segments for both preictal and interictal intervals.
The features extracted using CSP are used for training a linear discriminant analysis classifier, which is then employed in the testing phase.
A leave-one-out cross-validation strategy is adopted in the experiments.
The experimental results for seizure prediction obtained from the records of 24 patients from the CHB-MIT database reveal that the proposed predictor can achieve an average sensitivity of 0.89, an average false prediction rate of 0.39, and an average prediction time of 68.71 minutes using a 120-minute prediction horizon.
American Psychological Association (APA)
Alotaiby, Turky N.& Alshebeili, Saleh& Alotaibi, Faisal M.& Alrshoud, Saud R.. 2017. Epileptic Seizure Prediction Using CSP and LDA for Scalp EEG Signals. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1139834
Modern Language Association (MLA)
Alotaiby, Turky N.…[et al.]. Epileptic Seizure Prediction Using CSP and LDA for Scalp EEG Signals. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-11.
https://search.emarefa.net/detail/BIM-1139834
American Medical Association (AMA)
Alotaiby, Turky N.& Alshebeili, Saleh& Alotaibi, Faisal M.& Alrshoud, Saud R.. Epileptic Seizure Prediction Using CSP and LDA for Scalp EEG Signals. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1139834
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1139834