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A robust framework epileptic seizures classification based on lightweight structure deep convolutional neural network and wavelet decomposition

Nowadays scientific evidence suggests that epileptic seizures can appear in the brain signals minutes and even hours prior to their occurrence. Advances in predicting epileptic seizures can promise a robust model in which seizures and irreparable injuries at the time of occurrence can be possible. M...

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Main Authors: Nazanin Nemati, Saeed Meshgini, Ali Farzamnia
Format: Conference or Workshop Item
Language:English
English
Published: IEEE 2020
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:https://eprints.ums.edu.my/id/eprint/28946/1/A%20robust%20framework%20epileptic%20seizures%20classification%20based%20on%20lightweight%20structure%20deep%20convolutional%20neural%20network%20and%20wavelet%20decomposition%20FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/28946/3/A%20robust%20framework%20epileptic%20seizures%20classification%20based%20on%20lightweight%20structure%20deep%20convolutional%20neural%20network%20and%20wavelet%20decomposition%20ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/28946/
https://ieeexplore.ieee.org/document/9260797
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spelling my.ums.eprints.289462021-07-31T08:16:46Z https://eprints.ums.edu.my/id/eprint/28946/ A robust framework epileptic seizures classification based on lightweight structure deep convolutional neural network and wavelet decomposition Nazanin Nemati Saeed Meshgini Ali Farzamnia TK Electrical engineering. Electronics Nuclear engineering Nowadays scientific evidence suggests that epileptic seizures can appear in the brain signals minutes and even hours prior to their occurrence. Advances in predicting epileptic seizures can promise a robust model in which seizures and irreparable injuries at the time of occurrence can be possible. Most of the previous automated solutions are associated with challenges such as the lack of a proper signal descriptor, the existence of a large number of features and, consequently, the time-consuming analysis, which are not considering the uncertainty issue. In this paper, efficient and fastidious classification is performed by analysing the frequency bands of the input EEG signal via discrete wavelet transform, which is relying on the deep convolutional neural network based classification. Using the EEG signals obtained from the CHEG-MIT Scalp EEG database, the implementation in the desired model is performed and the results show that the proposed model has the best response in detecting the disease from the sample signal and with the highest level of certainty to follow. To solve the uncertainty problem, the repeatability algorithm test is arranged and after K-fold cross-validation, the experimental precision of all the three evaluation factors were equal to 99.34%, 99.53%, and 99.76%, respectively. IEEE 2020-08-06 Conference or Workshop Item PeerReviewed text en https://eprints.ums.edu.my/id/eprint/28946/1/A%20robust%20framework%20epileptic%20seizures%20classification%20based%20on%20lightweight%20structure%20deep%20convolutional%20neural%20network%20and%20wavelet%20decomposition%20FULL%20TEXT.pdf text en https://eprints.ums.edu.my/id/eprint/28946/3/A%20robust%20framework%20epileptic%20seizures%20classification%20based%20on%20lightweight%20structure%20deep%20convolutional%20neural%20network%20and%20wavelet%20decomposition%20ABSTRACT.pdf Nazanin Nemati and Saeed Meshgini and Ali Farzamnia (2020) A robust framework epileptic seizures classification based on lightweight structure deep convolutional neural network and wavelet decomposition. In: 2020 28th Iranian Conference on Electrical Engineering, ICEE 2020, 04 August 2020, University of Tabriz, Iran. https://ieeexplore.ieee.org/document/9260797
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Nazanin Nemati
Saeed Meshgini
Ali Farzamnia
A robust framework epileptic seizures classification based on lightweight structure deep convolutional neural network and wavelet decomposition
description Nowadays scientific evidence suggests that epileptic seizures can appear in the brain signals minutes and even hours prior to their occurrence. Advances in predicting epileptic seizures can promise a robust model in which seizures and irreparable injuries at the time of occurrence can be possible. Most of the previous automated solutions are associated with challenges such as the lack of a proper signal descriptor, the existence of a large number of features and, consequently, the time-consuming analysis, which are not considering the uncertainty issue. In this paper, efficient and fastidious classification is performed by analysing the frequency bands of the input EEG signal via discrete wavelet transform, which is relying on the deep convolutional neural network based classification. Using the EEG signals obtained from the CHEG-MIT Scalp EEG database, the implementation in the desired model is performed and the results show that the proposed model has the best response in detecting the disease from the sample signal and with the highest level of certainty to follow. To solve the uncertainty problem, the repeatability algorithm test is arranged and after K-fold cross-validation, the experimental precision of all the three evaluation factors were equal to 99.34%, 99.53%, and 99.76%, respectively.
format Conference or Workshop Item
author Nazanin Nemati
Saeed Meshgini
Ali Farzamnia
author_facet Nazanin Nemati
Saeed Meshgini
Ali Farzamnia
author_sort Nazanin Nemati
title A robust framework epileptic seizures classification based on lightweight structure deep convolutional neural network and wavelet decomposition
title_short A robust framework epileptic seizures classification based on lightweight structure deep convolutional neural network and wavelet decomposition
title_full A robust framework epileptic seizures classification based on lightweight structure deep convolutional neural network and wavelet decomposition
title_fullStr A robust framework epileptic seizures classification based on lightweight structure deep convolutional neural network and wavelet decomposition
title_full_unstemmed A robust framework epileptic seizures classification based on lightweight structure deep convolutional neural network and wavelet decomposition
title_sort robust framework epileptic seizures classification based on lightweight structure deep convolutional neural network and wavelet decomposition
publisher IEEE
publishDate 2020
url https://eprints.ums.edu.my/id/eprint/28946/1/A%20robust%20framework%20epileptic%20seizures%20classification%20based%20on%20lightweight%20structure%20deep%20convolutional%20neural%20network%20and%20wavelet%20decomposition%20FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/28946/3/A%20robust%20framework%20epileptic%20seizures%20classification%20based%20on%20lightweight%20structure%20deep%20convolutional%20neural%20network%20and%20wavelet%20decomposition%20ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/28946/
https://ieeexplore.ieee.org/document/9260797
_version_ 1760230654962827264
score 13.211869

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