Int J Neural Syst - Application of empirical mode decomposition (emd) for automated detection of epilepsy using EEG signals.

Tópicos

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Resumo

Epilepsy is a global disease with considerable incidence due to recurrent unprovoked seizures. These seizures can be noninvasively diagnosed using electroencephalogram (EEG), a measure of neuronal electrical activity in brain recorded along scalp. EEG is highly nonlinear, nonstationary and non-Gaussian in nature. Nonlinear adaptive models such as empirical mode decomposition (EMD) provide intuitive understanding of information present in these signals. In this study a novel methodology is proposed to automatically classify EEG of normal, inter-ictal and ictal subjects using EMD decomposition. EEG decomposition using EMD yields few intrinsic mode functions (IMF), which are amplitude and frequency modulated (AM and FM) waves. Hilbert transform of these IMF provides AM and FM frequencies. Features such as spectral peaks, spectral entropy and spectral energy in each IMF are extracted and fed to decision tree classifier for automated diagnosis. In this work, we have compared the performance of classification using two types of decision trees (i) classification and regression tree (CART) and (ii) C4.5. We have obtained the highest average accuracy of 95.33%, average sensitivity of 98%, and average specificity of 97% using C4.5 decision tree classifier. The developed methodology is ready for clinical validation on large databases and can be deployed for mass screening.

Resumo Limpo

epilepsi global diseas consider incid due recurr unprovok seizur seizur can noninvas diagnos use electroencephalogram eeg measur neuron electr activ brain record along scalp eeg high nonlinear nonstationari nongaussian natur nonlinear adapt model empir mode decomposit emd provid intuit understand inform present signal studi novel methodolog propos automat classifi eeg normal interict ictal subject use emd decomposit eeg decomposit use emd yield intrins mode function imf amplitud frequenc modul fm wave hilbert transform imf provid fm frequenc featur spectral peak spectral entropi spectral energi imf extract fed decis tree classifi autom diagnosi work compar perform classif use two type decis tree classif regress tree cart ii c obtain highest averag accuraci averag sensit averag specif use c decis tree classifi develop methodolog readi clinic valid larg databas can deploy mass screen

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