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Novel approaches for automated epileptic diagnosis using fcbf selection and classification algorithms

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2013年第Sup1期21卷 2092-2109页

作者： Sen, Baha Peker, Musa Yildirim Beyazit Univ Fac Engn Dept Comp Engn Ankara Turkey Karabuk Univ Fac Engn Dept Comp Engn Karabuk Turkey

This paper presents a new application for automated epileptic detection using the fast correlation-based feature (fcbf) selection and classification algorithms. This study consists of 3 stages: feature extraction, feature selection from electroencephalography (EEG) signals, and the classification of these signals. In the feature extraction phase, 16 attribute algorithms are used in 5 categories, and 36 feature parameters are obtained from these algorithms. In the feature selection phase, the fcbf algorithm is chosen to select a set of attributes that best represent the EEG signals. The resulting attributes are used as input parameters for the classification algorithms. In the classification phase, the problem is classified with 6 different classification algorithms. The results obtained with the different classification algorithms are provided in order to compare the calculation times and the accuracy rates. The evolution of the proposed system is conducted using k-fold cross-validation, classification accuracy, sensitivity and specificity values, and a confusion matrix. The proposed approach enables 100% classification accuracy with the use of the multilayer perceptron neural network and naive Bayes algorithm. The stated results show that the proposed method is capable of designing a new intelligent assistance diagnostic system.

关键词： EEG signals classification algorithms fcbf selection algorithm epilepsy disease

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