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Computational Intelligence and Neuroscience
Volume 2017 (2017), Article ID 1240323, 11 pages
https://doi.org/10.1155/2017/1240323
Research Article

Epileptic Seizure Prediction Using CSP and LDA for Scalp EEG Signals

1KACST, Riyadh, Saudi Arabia
2KACST-TIC in Radio Frequency and Photonics for the e-Society (RFTONICS), Electrical Engineering Department, King Saud University, Riyadh, Saudi Arabia

Correspondence should be addressed to Turky N. Alotaiby

Received 26 April 2017; Revised 15 August 2017; Accepted 4 October 2017; Published 31 October 2017

Academic Editor: Pedro Antonio Gutierrez

Copyright © 2017 Turky N. Alotaiby et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.