TY - GEN
T1 - Principal component analysis-based neural network with fuzzy membership function for epileptic seizure detection
AU - Fatichah, Chastine
AU - Iliyasu, Abdullah M.
AU - Abuhasel, Khaled A.
AU - Suciati, Nanik
AU - Al-Qodah, Mohammed A.
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014
Y1 - 2014
N2 - A hybrid principal component analysis (PCA)-based neural network with fuzzy membership function (NEWFM) is proposed for epileptic seizure detection. By combining PCA and NEWFM, the proposed method improves the accuracy in epileptic seizure detection. The PCA is used for wavelet feature enhancement needed to eliminate the sensitivity of noise, electrode artifacts, or redundancy. NEWFM, a model of neural networks, is integrated to improve prediction results by updating weights of fuzzy membership functions. A dataset made up of 5 sets, each consisting 100 single EEGs segments, is employed to evaluate the proposed system's performance. Based on the experiments, the prediction results show an accuracy rate of 98.29% for epileptic seizure classification while in the best cases the accuracy reaches 99.5% for the 'normal' (Z-S) seizure classification task.
AB - A hybrid principal component analysis (PCA)-based neural network with fuzzy membership function (NEWFM) is proposed for epileptic seizure detection. By combining PCA and NEWFM, the proposed method improves the accuracy in epileptic seizure detection. The PCA is used for wavelet feature enhancement needed to eliminate the sensitivity of noise, electrode artifacts, or redundancy. NEWFM, a model of neural networks, is integrated to improve prediction results by updating weights of fuzzy membership functions. A dataset made up of 5 sets, each consisting 100 single EEGs segments, is employed to evaluate the proposed system's performance. Based on the experiments, the prediction results show an accuracy rate of 98.29% for epileptic seizure classification while in the best cases the accuracy reaches 99.5% for the 'normal' (Z-S) seizure classification task.
KW - Discrete wavelet trasform
KW - Epilepsy
KW - Epileptic seizure detection
KW - Fuzzy membership
KW - Neural network
KW - PCA
UR - http://www.scopus.com/inward/record.url?scp=84926630363&partnerID=8YFLogxK
U2 - 10.1109/ICNC.2014.6975832
DO - 10.1109/ICNC.2014.6975832
M3 - Conference contribution
AN - SCOPUS:84926630363
T3 - 2014 10th International Conference on Natural Computation, ICNC 2014
SP - 186
EP - 191
BT - 2014 10th International Conference on Natural Computation, ICNC 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 10th International Conference on Natural Computation, ICNC 2014
Y2 - 19 August 2014 through 21 August 2014
ER -