Int J Neural Syst - Comparison of ictal and interictal EEG signals using fractal features.

Tópicos

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Resumo

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.

Resumo Limpo

featur analysi epilept eeg signific diagnosi epilepsi paper introduc two nonlinear featur deriv fractal geometri epilept eeg analysi featur blanket dimens fractal intercept extract character behavior eeg activ discriminatori power ictal interict eeg compar mean statist method found signific differ blanket dimens fractal intercept interict ictal eeg differ fractal intercept featur interict ictal eeg notic blanket dimens featur furthermor two fractal featur multiscal combin support vector machin svm achiev accuraci ictal interict eeg classif normal ictal interict eeg classif

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