TY - GEN
T1 - Robust epileptic seizure classification
AU - Alzami, Farrikh
AU - Wang, Daxing
AU - Yu, Zhiwen
AU - You, Jia
AU - Wong, Hau San
AU - Han, Guoqiang
PY - 2016/1/1
Y1 - 2016/1/1
N2 - A lot of feature vectors and sub-band signals are considered for Epileptic seizure classification. Unfortunately, not all the feature vectors and sub-band signals contribute to the final result. In view of this limitation, we propose a modified Differential Evolution Feature Selection algorithm (MDEFS), which searches the best feature vector subset and the sub-band signals to distinguish three groups of subjects (healthy, ictal and interictal). From the experiment results, it is observed that the bagging method based on the optimal feature subset (the standard deviation attribute in the delta sub-band signal, the time-lag attribute in the delta sub-band signal, fractal dimension in the alpha sub-band signal, the correlation dimension attribute in the alpha sub-band signal and the standard deviation attribute in the beta sub-band signal) selected by MDEFS results in highest classification accuracy of 98.67 %.
AB - A lot of feature vectors and sub-band signals are considered for Epileptic seizure classification. Unfortunately, not all the feature vectors and sub-band signals contribute to the final result. In view of this limitation, we propose a modified Differential Evolution Feature Selection algorithm (MDEFS), which searches the best feature vector subset and the sub-band signals to distinguish three groups of subjects (healthy, ictal and interictal). From the experiment results, it is observed that the bagging method based on the optimal feature subset (the standard deviation attribute in the delta sub-band signal, the time-lag attribute in the delta sub-band signal, fractal dimension in the alpha sub-band signal, the correlation dimension attribute in the alpha sub-band signal and the standard deviation attribute in the beta sub-band signal) selected by MDEFS results in highest classification accuracy of 98.67 %.
KW - Bagging
KW - Differential evolution
KW - Discrete Wavelet Transform
KW - Epileptic seizure classification
KW - Feature selection
UR - http://www.scopus.com/inward/record.url?scp=84978807315&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-42294-7_32
DO - 10.1007/978-3-319-42294-7_32
M3 - Conference article published in proceeding or book
SN - 9783319422930
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 363
EP - 373
BT - Intelligent Computing Theories and Application - 12th International Conference, ICIC 2016, Proceedings
PB - Springer Verlag
T2 - 12th International Conference on Intelligent Computing Theories and Application, ICIC 2016
Y2 - 2 August 2016 through 5 August 2016
ER -