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Epileptic seizure classification of EEG time-series using rational discrete short-time fourier transform

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Details

Original languageEnglish
Article number6909003
Pages (from-to)541-552
Number of pages12
JournalIEEE Transactions on Biomedical Engineering
Volume62
Issue number2
DOIs
Publication statusPublished - 1 Feb 2015
Publication typeA1 Journal article-refereed

Abstract

A system for epileptic seizure detection in electroencephalography (EEG) is described in this paper. One of the challenges is to distinguish rhythmic discharges from nonstationary patterns occurring during seizures. The proposed approach is based on an adaptive and localized time-frequency representation of EEG signals by means of rational functions. The corresponding rational discrete short-time Fourier transform (DSTFT) is a novel feature extraction technique for epileptic EEG data. A multilayer perceptron classifier is fed by the coefficients of the rational DSTFT in order to separate seizure epochs from seizure-free epochs. The effectiveness of the proposed method is compared with several state-of-art feature extraction algorithms used in offline epileptic seizure detection. The results of the comparative evaluations show that the proposed method outperforms competing techniques in terms of classification accuracy. In addition, it provides a compact representation of EEG time-series.

ASJC Scopus subject areas

Keywords

  • EEG, Malnquist-Takenaka system, Rational functions, Seizure classification, Time-frequency analysis

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