Automatic detection of epileptic seizure using time-frequency distributions

H. R. Mohseni; A. Maghsoudi; M. H. Kadbi; J. Hashemi; A. Ashourvan

doi:10.1049/cp:20060378

Automatic detection of epileptic seizure using time-frequency distributions

H. R. Mohseni, A. Maghsoudi, M. H. Kadbi, J. Hashemi, A. Ashourvan

Engineering Science and Mechanics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

14 Scopus citations

Abstract

The aim of this work is to introduce a new method based on time frequency distribution for classifying the EEG signals. Some parameters are extracted using time-frequency distribution as inputs to a feed-forward backpropagation neural networks (FBNN). The proposed method had better results with 98.25% accuracy compared to previously studied methods such as wavelet transform, entropy, logistic regression and Lyapunov exponent.

Original language	English (US)
Title of host publication	IET 3rd International Conference MEDSIP 2006
Subtitle of host publication	Advances in Medical, Signal and Information Processing
Pages	29
Number of pages	1
Edition	520
DOIs	https://doi.org/10.1049/cp:20060378
State	Published - 2006
Event	IET 3rd International Conference MEDSIP 2006: Advances in Medical, Signal and Information Processing - Glasgow, United Kingdom Duration: Jul 17 2006 → Jul 19 2006

Publication series

Name	IET Conference Publications
Number	520

Conference

Conference	IET 3rd International Conference MEDSIP 2006: Advances in Medical, Signal and Information Processing
Country/Territory	United Kingdom
City	Glasgow
Period	7/17/06 → 7/19/06

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering

Access to Document

10.1049/cp:20060378

Cite this

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title = "Automatic detection of epileptic seizure using time-frequency distributions",

abstract = "The aim of this work is to introduce a new method based on time frequency distribution for classifying the EEG signals. Some parameters are extracted using time-frequency distribution as inputs to a feed-forward backpropagation neural networks (FBNN). The proposed method had better results with 98.25% accuracy compared to previously studied methods such as wavelet transform, entropy, logistic regression and Lyapunov exponent.",

author = "Mohseni, {H. R.} and A. Maghsoudi and Kadbi, {M. H.} and J. Hashemi and A. Ashourvan",

note = "Copyright: Copyright 2008 Elsevier B.V., All rights reserved.; IET 3rd International Conference MEDSIP 2006: Advances in Medical, Signal and Information Processing ; Conference date: 17-07-2006 Through 19-07-2006",

year = "2006",

doi = "10.1049/cp:20060378",

language = "English (US)",

isbn = "0863416586",

series = "IET Conference Publications",

number = "520",

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booktitle = "IET 3rd International Conference MEDSIP 2006",

edition = "520",

}

Mohseni, HR, Maghsoudi, A, Kadbi, MH, Hashemi, J & Ashourvan, A 2006, Automatic detection of epileptic seizure using time-frequency distributions. in IET 3rd International Conference MEDSIP 2006: Advances in Medical, Signal and Information Processing. 520 edn, IET Conference Publications, no. 520, pp. 29, IET 3rd International Conference MEDSIP 2006: Advances in Medical, Signal and Information Processing, Glasgow, United Kingdom, 7/17/06. https://doi.org/10.1049/cp:20060378

Automatic detection of epileptic seizure using time-frequency distributions. / Mohseni, H. R.; Maghsoudi, A.; Kadbi, M. H. et al.
IET 3rd International Conference MEDSIP 2006: Advances in Medical, Signal and Information Processing. 520. ed. 2006. p. 29 (IET Conference Publications; No. 520).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Automatic detection of epileptic seizure using time-frequency distributions

AU - Mohseni, H. R.

AU - Maghsoudi, A.

AU - Kadbi, M. H.

AU - Hashemi, J.

AU - Ashourvan, A.

PY - 2006

Y1 - 2006

N2 - The aim of this work is to introduce a new method based on time frequency distribution for classifying the EEG signals. Some parameters are extracted using time-frequency distribution as inputs to a feed-forward backpropagation neural networks (FBNN). The proposed method had better results with 98.25% accuracy compared to previously studied methods such as wavelet transform, entropy, logistic regression and Lyapunov exponent.

AB - The aim of this work is to introduce a new method based on time frequency distribution for classifying the EEG signals. Some parameters are extracted using time-frequency distribution as inputs to a feed-forward backpropagation neural networks (FBNN). The proposed method had better results with 98.25% accuracy compared to previously studied methods such as wavelet transform, entropy, logistic regression and Lyapunov exponent.

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U2 - 10.1049/cp:20060378

DO - 10.1049/cp:20060378

M3 - Conference contribution

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SN - 9780863416583

T3 - IET Conference Publications

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BT - IET 3rd International Conference MEDSIP 2006

T2 - IET 3rd International Conference MEDSIP 2006: Advances in Medical, Signal and Information Processing

Y2 - 17 July 2006 through 19 July 2006

ER -

Automatic detection of epileptic seizure using time-frequency distributions

Abstract

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Conference

All Science Journal Classification (ASJC) codes

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