title: Comparison of ictal and interictal EEG signals using fractal features.
abstract: The feature analysis of epileptic EEG is very significant in diagnosis of epilepsy. This paper introduces two nonlinear features derived from fractal geometry for epileptic EEG analysis. The features of blanket dimension and fractal intercept are extracted to characterize behavior of EEG activities, and then their discriminatory power for ictal and interictal EEGs are compared by means of statistical methods. It is found that there is significant difference of the blanket dimension and fractal intercept between interictal and ictal EEGs, and the difference of the fractal intercept feature between interictal and ictal EEGs is more noticeable than the blanket dimension feature. Furthermore, these two fractal features at multi-scales are combined with support vector machine (SVM) to achieve accuracies of 97.58% for ictal and interictal EEG classification and 97.13% for normal, ictal and interictal EEG classification.
authors.affiliation: School of Information Science and Engineering, Shandong University, 27 Shanda Road, Jinan 250100, P. R. China.
authors.forename: Yu
authors.initials: Y
authors.lastname: Wang
authors.forename: Weidong
authors.initials: W
authors.lastname: Zhou
authors.forename: Qi
authors.initials: Q
authors.lastname: Yuan
authors.forename: Xueli
authors.initials: X
authors.lastname: Li
authors.forename: Qingfang
authors.initials: Q
authors.lastname: Meng
authors.forename: Xiuhe
authors.initials: X
authors.lastname: Zhao
authors.forename: Jiwen
authors.initials: J
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D001921:Brain / Q000503:physiopathology*; D004569:Electroencephalography; D004827:Epilepsy / Q000503:physiopathology*; D017709:Fractals; D006801:Humans; D012815:Signal Processing, Computer-Assisted