TY - JOUR
T1 - A novel method for automated diagnosis of epilepsy using complex-valued classifiers
AU - Peker, Musa
AU - Sen, Baha
AU - Delen, Dursun
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2016/1/1
Y1 - 2016/1/1
N2 - The study reported herein proposes a new method for the diagnosis of epilepsy from electroencephalography (EEG) signals based on complex classifiers. To carry out this study, first the features of EEG data are extracted using a dual-tree complex wavelet transformation at different levels of granularity to obtain size reduction. In subsequent phases, five features (based on statistical measurements maximum value, minimum value, arithmetic mean, standard deviation, median value) are obtained by using the feature vectors, and are presented as the input dimension to the complex-valued neural networks. The evaluation of the proposed method is conducted using the k-fold cross-validation methodology, reporting on classification accuracy, sensitivity, and specificity. The proposed method is tested using a benchmark EEG dataset, and high accuracy rates were obtained. The stated results show that the proposed method can be used to design an accurate classification system for epilepsy diagnosis.
AB - The study reported herein proposes a new method for the diagnosis of epilepsy from electroencephalography (EEG) signals based on complex classifiers. To carry out this study, first the features of EEG data are extracted using a dual-tree complex wavelet transformation at different levels of granularity to obtain size reduction. In subsequent phases, five features (based on statistical measurements maximum value, minimum value, arithmetic mean, standard deviation, median value) are obtained by using the feature vectors, and are presented as the input dimension to the complex-valued neural networks. The evaluation of the proposed method is conducted using the k-fold cross-validation methodology, reporting on classification accuracy, sensitivity, and specificity. The proposed method is tested using a benchmark EEG dataset, and high accuracy rates were obtained. The stated results show that the proposed method can be used to design an accurate classification system for epilepsy diagnosis.
UR - http://www.scopus.com/inward/record.url?scp=84971371361&partnerID=8YFLogxK
U2 - 10.1109/JBHI.2014.2387795
DO - 10.1109/JBHI.2014.2387795
M3 - Article
C2 - 25576585
AN - SCOPUS:84971371361
SN - 2168-2194
VL - 20
SP - 108
EP - 118
JO - IEEE Journal of Biomedical and Health Informatics
JF - IEEE Journal of Biomedical and Health Informatics
IS - 1
M1 - 7001559
ER -