Med Biol Eng Comput - Wavelet-based sparse functional linear model with applications to EEGs seizure detection and epilepsy diagnosis.

Tópicos

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Resumo

In epilepsy diagnosis or epileptic seizure detection, much effort has been focused on finding effective combination of feature extraction and classification methods. In this paper, we develop a wavelet-based sparse functional linear model for representation of EEG signals. The aim of this modeling approach is to capture discriminative random components of EEG signals using wavelet variances. To achieve this goal, a forward search algorithm is proposed for determination of an appropriate wavelet decomposition level. Two EEG databases from University of Bonn and University of Freiburg are used for illustration of applicability of the proposed method to both epilepsy diagnosis and epileptic seizure detection problems. For this data considered, we show that wavelet-based sparse functional linear model with a simple classifier such as 1-NN classification method leads to higher classification results than those obtained using other complicated methods such as support vector machine. This approach produces a 100% classification accuracy for various classification tasks using the EEG database from University of Bonn, and outperforms many other state-of-the-art techniques. The proposed classification scheme leads to 99% overall classification accuracy for the EEG data from University of Freiburg.

Resumo Limpo

epilepsi diagnosi epilept seizur detect much effort focus find effect combin featur extract classif method paper develop waveletbas spars function linear model represent eeg signal aim model approach captur discrimin random compon eeg signal use wavelet varianc achiev goal forward search algorithm propos determin appropri wavelet decomposit level two eeg databas univers bonn univers freiburg use illustr applic propos method epilepsi diagnosi epilept seizur detect problem data consid show waveletbas spars function linear model simpl classifi nn classif method lead higher classif result obtain use complic method support vector machin approach produc classif accuraci various classif task use eeg databas univers bonn outperform mani stateoftheart techniqu propos classif scheme lead overal classif accuraci eeg data univers freiburg

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