Current Proceedings on Technology

Yazarlar: Musa PEKER

Konular:-

Anahtar Kelimeler:Complex-valued neural network,EEG signals,Epilepsy disease

Özet: The study proposes a new method for the diagnosis of epilepsy from EEG signals based on complex classifiers. First of all, 8 effective feature extraction algorithms were used in order to identify meaningful information from EEG signals. In later phases, 8 feature values were presented as introduction to the complex valued neural network (CVANN). Two different classification experiments were undertaken with the help of the developed model: 1) the classification of healthy volunteers, epilepsy patients during seizure and epilepsy patients during a seizure-free interval, 2) the classification of epilepsy patients during seizure and healthy volunteers. The evolution of the proposed system is conducted using k-fold cross-validation, classification accuracy (CA), sensitivity and specificity values. The proposed approach identified EEG signals with 97.01% and 100% accuracy in the first and second experiments respectively. The stated results show that the proposed method is capable of designing a new intelligent assistance diagnostic system.

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