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

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Epileptic seizure classification of EEG time-series using rational discrete short-time fourier transform. / Samiee, Kaveh; Kovacs, Peter; Gabbouj, Moncef.

julkaisussa: IEEE Transactions on Biomedical Engineering, Vuosikerta 62, Nro 2, 6909003, 01.02.2015, s. 541-552.

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Samiee, Kaveh ; Kovacs, Peter ; Gabbouj, Moncef. / Epileptic seizure classification of EEG time-series using rational discrete short-time fourier transform. Julkaisussa: IEEE Transactions on Biomedical Engineering. 2015 ; Vuosikerta 62, Nro 2. Sivut 541-552.

Bibtex - Lataa

@article{7cb21b52ac034fe7ab70312d12f56c30,

title = "Epileptic seizure classification of EEG time-series using rational discrete short-time fourier transform",

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.",

keywords = "EEG, Malnquist-Takenaka system, Rational functions, Seizure classification, Time-frequency analysis",

author = "Kaveh Samiee and Peter Kovacs and Moncef Gabbouj",

note = "Contribution: organisation=sgn,FACT1=1<br/>Portfolio EDEND: 2015-01-15<br/>Publisher name: Institute of Electrical and Electronics Engineers",

year = "2015",

month = "2",

day = "1",

doi = "10.1109/TBME.2014.2360101",

language = "English",

volume = "62",

pages = "541--552",

journal = "IEEE Transactions on Biomedical Engineering",

issn = "0018-9294",

publisher = "Institute of Electrical and Electronics Engineers",

number = "2",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - Epileptic seizure classification of EEG time-series using rational discrete short-time fourier transform

AU - Samiee, Kaveh

AU - Kovacs, Peter

AU - Gabbouj, Moncef

N1 - Contribution: organisation=sgn,FACT1=1<br/>Portfolio EDEND: 2015-01-15<br/>Publisher name: Institute of Electrical and Electronics Engineers

PY - 2015/2/1

Y1 - 2015/2/1

N2 - 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.

AB - 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.

KW - EEG

KW - Malnquist-Takenaka system

KW - Rational functions

KW - Seizure classification

KW - Time-frequency analysis

U2 - 10.1109/TBME.2014.2360101

DO - 10.1109/TBME.2014.2360101

M3 - Article

VL - 62

SP - 541

EP - 552

JO - IEEE Transactions on Biomedical Engineering

JF - IEEE Transactions on Biomedical Engineering

SN - 0018-9294

IS - 2

M1 - 6909003

ER -

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