e-journal

A New Framework Based on Recurrence Quantification Analysis for Epileptic Seizure Detection

---

Abstract—This study presents applying recurrence quantificationanalysis(RQA)onEEGrecordingsandtheirsubbands:delta, theta,alpha,beta,andgammaforepilepticseizuredetection.RQA isadoptedsinceitdoesnotrequireassumptionsaboutstationarity, lengthofsignal,andnoise.ThedecompositionoftheoriginalEEG into its five constituent subbands helps better identification of the dynamical system of EEG signal. This leads to better classification of the database into three groups: Healthy subjects, epileptic subjects during a seizure-free interval (Interictal) and epileptic subjects during a seizure course (Ictal). The proposed algorithm is applied to an epileptic EEG dataset provided by Dr. R. Andrzejak of the Epilepsy Center, University of Bonn, Bonn, Germany. CombinationofRQA-basedmeasuresoftheoriginalsignalandits subbands results in an overall accuracy of 98.67% that indicates high accuracy of the proposed method.

IndexTerms—EEGsubbands,Epilepticseizuredetection,phase space reconstruction, recurrence quantification analysis (RQA), wavelet decomposition.

---

Ketersediaan

Tidak ada salinan data

Informasi Detail

Judul Seri

-

No. Panggil

-

Penerbit

: IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS., 2013

Deskripsi Fisik

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, VOL. 17, NO. 3, MAY 2013 p. 572-578

Bahasa

English

ISBN/ISSN

2168-2194

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

EPILEPSI
TEKNOLOGI KEDOKTERAN

Info Detail Spesifik

-

Pernyataan Tanggungjawab

Niknazar, M. ... [et al.]

Versi lain/terkait

Lampiran Berkas

Komentar

---

Anda harus masuk sebelum memberikan komentar