A novel method for automated diagnosis of epilepsy using complex-valued classifiers

Musa Peker, Baha Sen, Dursun Delen

Research output: Contribution to journalArticlepeer-review

173 Scopus citations


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.

Original languageEnglish
Article number7001559
Pages (from-to)108-118
Number of pages11
JournalIEEE Journal of Biomedical and Health Informatics
Issue number1
StatePublished - 1 Jan 2016
Externally publishedYes


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