Comput Methods Programs Biomed - Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition.

Tópicos

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Resumo

Epilepsy is one of the most common neurological disorders characterized by transient and unexpected electrical disturbance of the brain. The electroencephalogram (EEG) is an invaluable measurement for the purpose of assessing brain activities, containing information relating to the different physiological states of the brain. It is a very effective tool for understanding the complex dynamical behavior of the brain. This paper presents the application of empirical mode decomposition (EMD) for analysis of EEG signals. The EMD decomposes a EEG signal into a finite set of bandlimited signals termed intrinsic mode functions (IMFs). The Hilbert transformation of IMFs provides analytic signal representation of IMFs. The area measured from the trace of the analytic IMFs, which have circular form in the complex plane, has been used as a feature in order to discriminate normal EEG signals from the epileptic seizure EEG signals. It has been shown that the area measure of the IMFs has given good discrimination performance. Simulation results illustrate the effectiveness of the proposed method.

Resumo Limpo

epilepsi one common neurolog disord character transient unexpect electr disturb brain electroencephalogram eeg invalu measur purpos assess brain activ contain inform relat differ physiolog state brain effect tool understand complex dynam behavior brain paper present applic empir mode decomposit emd analysi eeg signal emd decompos eeg signal finit set bandlimit signal term intrins mode function imf hilbert transform imf provid analyt signal represent imf area measur trace analyt imf circular form complex plane use featur order discrimin normal eeg signal epilept seizur eeg signal shown area measur imf given good discrimin perform simul result illustr effect propos method

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