TY - JOUR A2 - Gutierrez, Pedro Antonio AU - Alotaiby, Turky N. AU - Alshebeili, Saleh A. AU - Alotaibi, Faisal M. AU - Alrshoud, Saud R. PY - 2017 DA - 2017/10/31 TI - Epileptic Seizure Prediction Using CSP and LDA for Scalp EEG Signals SP - 1240323 VL - 2017 AB - 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. SN - 1687-5265 UR - https://doi.org/10.1155/2017/1240323 DO - 10.1155/2017/1240323 JF - Computational Intelligence and Neuroscience PB - Hindawi KW - ER -