Comput Methods Programs Biomed - Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions.

Tópicos

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Resumo

Epilepsy is a neurological disorder which is characterized by transient and unexpected electrical disturbance of the brain. The electroencephalogram (EEG) is a commonly used signal for detection of epileptic seizures. This paper presents a new method for classification of ictal and seizure-free EEG signals. The proposed method is based on the empirical mode decomposition (EMD) and the second-order difference plot (SODP). The EMD method decomposes an EEG signal into a set of symmetric and band-limited signals termed as intrinsic mode functions (IMFs). The SODP of IMFs provides elliptical structure. The 95% confidence ellipse area measured from the SODP of IMFs has been used as a feature in order to discriminate seizure-free EEG signals from the epileptic seizure EEG signals. The feature space obtained from the ellipse area parameters of two IMFs has been used for classification of ictal and seizure-free EEG signals using the artificial neural network (ANN) classifier. It has been shown that the feature space formed using ellipse area parameters of first and second IMFs has given good classification performance. Experimental results on EEG database available by the University of Bonn, Germany, are included to illustrate the effectiveness of the proposed method.

Resumo Limpo

epilepsi neurolog disord character transient unexpect electr disturb brain electroencephalogram eeg common use signal detect epilept seizur paper present new method classif ictal seizurefre eeg signal propos method base empir mode decomposit emd secondord differ plot sodp emd method decompos eeg signal set symmetr bandlimit signal term intrins mode function imf sodp imf provid ellipt structur confid ellips area measur sodp imf use featur order discrimin seizurefre eeg signal epilept seizur eeg signal featur space obtain ellips area paramet two imf use classif ictal seizurefre eeg signal use artifici neural network ann classifi shown featur space form use ellips area paramet first second imf given good classif perform experiment result eeg databas avail univers bonn germani includ illustr effect propos method

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