TY - GEN
T1 - Intra-patient and inter-patient seizure prediction from spatial-temporal EEG features
AU - Ma, Shuoxin
AU - Bliss, Daniel
N1 - Publisher Copyright:
© 2014 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2015/4/24
Y1 - 2015/4/24
N2 - In this paper, an algorithm for both intra-patient and inter-patient seizure prediction from invasive electroencephalography (EEG) is proposed and tested. Multi-channel EEG signal are pre-processed, windowed and built into spatial-temporal covariance matrices. Multivariate features are extracted from these matrices, then reduced in dimensionality by principle component analysis (PCA). A support vector machine (SVM) system is trained with the features of classified segments of data to predict the un-classified segments. The cross-validation test shows that the proposed algorithm achieves significantly better performance than that achieved in existing literatures, with the area under receiver operating characteristic (ROC) curve of 0.977 for intra-patient and 0.822 for inter-patient prediction. The significance test further proves that the result is statistically reliable for intra-patient prediction with p-value of 0.00, and well considerable for inter-patient prediction with p-value of 0.08.
AB - In this paper, an algorithm for both intra-patient and inter-patient seizure prediction from invasive electroencephalography (EEG) is proposed and tested. Multi-channel EEG signal are pre-processed, windowed and built into spatial-temporal covariance matrices. Multivariate features are extracted from these matrices, then reduced in dimensionality by principle component analysis (PCA). A support vector machine (SVM) system is trained with the features of classified segments of data to predict the un-classified segments. The cross-validation test shows that the proposed algorithm achieves significantly better performance than that achieved in existing literatures, with the area under receiver operating characteristic (ROC) curve of 0.977 for intra-patient and 0.822 for inter-patient prediction. The significance test further proves that the result is statistically reliable for intra-patient prediction with p-value of 0.00, and well considerable for inter-patient prediction with p-value of 0.08.
UR - http://www.scopus.com/inward/record.url?scp=84940507126&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84940507126&partnerID=8YFLogxK
U2 - 10.1109/ACSSC.2014.7094426
DO - 10.1109/ACSSC.2014.7094426
M3 - Conference contribution
AN - SCOPUS:84940507126
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 194
EP - 199
BT - Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers
A2 - Matthews, Michael B.
PB - IEEE Computer Society
T2 - 48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
Y2 - 2 November 2014 through 5 November 2014
ER -