e-journal

Detection of Seizure and Epilepsy Using Higher Order Statistics in the EMD Domain

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Abstract—In this paper, a method using higher order statistical moments of EEG signals calculated in the empirical mode decomposition (EMD) domain is proposed for detecting seizure and epilepsy. The appropriateness of these moments in distinguishing the EEG signals is investigated through an extensive analysis in the EMD domain. An artificial neural network is employed as the classifier of the EEG signals wherein these moments are used as features.Theperformanceoftheproposedmethodisstudiedusing a publicly available benchmark database for various classification casesthatincludehealthy,interictal(seizure-freeinterval)andictal (seizure), healthy and seizure, nonseizure and seizure, and interictal and ictal, and compared with that of several recent methods based on time–frequency analysis and statistical moments. It is shown that the proposed method can provide, in almost all the cases, 100% accuracy, sensitivity, and specificity, especially in the case of discriminating seizure activities from the nonseizure ones for patients with epilepsy while being much faster as compared to the time–frequency analysis-based techniques.

Index Terms—Electroencephalogram (EEG), empirical mode decomposition (EMD), epileptic seizure, neural network.

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Informasi Detail

Judul Seri

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No. Panggil

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Penerbit

: IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS., 2013

Deskripsi Fisik

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, VOL. 17, NO. 2, MARCH 2013 p. 312-318

Bahasa

English

ISBN/ISSN

2168-2194

Klasifikasi

NONE

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

KEDOKTERAN-ALAT DAN PERLENGKAPAN
EPILEPSI

Info Detail Spesifik

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Pernyataan Tanggungjawab

S. M. Shafiul Alam and M. I. H. Bhuiyan

Versi lain/terkait

Lampiran Berkas

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