Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features

Epilepsy is a brain disorder disease that affects people’s quality of life. Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper provides a computer-aided diagnosis system (CADS) for the automatic diagnosis of epileptic seizures in EEG signals. The proposed method...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Anis Malekzadeh, Assef Zare, Mahdi Yaghoobi, Hamid-Reza Kobravi, Roohallah Alizadehsani
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	epileptic seizures EEG diagnosis TQWT nonlinear features CNN–RNN Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/898912f40c244380b138ec817be2383f
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:898912f40c244380b138ec817be2383f
record_format	dspace
spelling	oai:doaj.org-article:898912f40c244380b138ec817be2383f2021-11-25T18:58:36ZEpileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features10.3390/s212277101424-8220https://doaj.org/article/898912f40c244380b138ec817be2383f2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7710https://doaj.org/toc/1424-8220Epilepsy is a brain disorder disease that affects people’s quality of life. Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper provides a computer-aided diagnosis system (CADS) for the automatic diagnosis of epileptic seizures in EEG signals. The proposed method consists of three steps, including preprocessing, feature extraction, and classification. In order to perform the simulations, the Bonn and Freiburg datasets are used. Firstly, we used a band-pass filter with 0.5–40 Hz cut-off frequency for removal artifacts of the EEG datasets. Tunable-Q Wavelet Transform (TQWT) is used for EEG signal decomposition. In the second step, various linear and nonlinear features are extracted from TQWT sub-bands. In this step, various statistical, frequency, and nonlinear features are extracted from the sub-bands. The nonlinear features used are based on fractal dimensions (FDs) and entropy theories. In the classification step, different approaches based on conventional machine learning (ML) and deep learning (DL) are discussed. In this step, a CNN–RNN-based DL method with the number of layers proposed is applied. The extracted features have been fed to the input of the proposed CNN–RNN model, and satisfactory results have been reported. In the classification step, the K-fold cross-validation with k = 10 is employed to demonstrate the effectiveness of the proposed CNN–RNN classification procedure. The results revealed that the proposed CNN–RNN method for Bonn and Freiburg datasets achieved an accuracy of 99.71% and 99.13%, respectively.Anis MalekzadehAssef ZareMahdi YaghoobiHamid-Reza KobraviRoohallah AlizadehsaniMDPI AGarticleepileptic seizuresEEGdiagnosisTQWTnonlinear featuresCNN–RNNChemical technologyTP1-1185ENSensors, Vol 21, Iss 7710, p 7710 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	epileptic seizures EEG diagnosis TQWT nonlinear features CNN–RNN Chemical technology TP1-1185
spellingShingle	epileptic seizures EEG diagnosis TQWT nonlinear features CNN–RNN Chemical technology TP1-1185 Anis Malekzadeh Assef Zare Mahdi Yaghoobi Hamid-Reza Kobravi Roohallah Alizadehsani Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features
description	Epilepsy is a brain disorder disease that affects people’s quality of life. Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper provides a computer-aided diagnosis system (CADS) for the automatic diagnosis of epileptic seizures in EEG signals. The proposed method consists of three steps, including preprocessing, feature extraction, and classification. In order to perform the simulations, the Bonn and Freiburg datasets are used. Firstly, we used a band-pass filter with 0.5–40 Hz cut-off frequency for removal artifacts of the EEG datasets. Tunable-Q Wavelet Transform (TQWT) is used for EEG signal decomposition. In the second step, various linear and nonlinear features are extracted from TQWT sub-bands. In this step, various statistical, frequency, and nonlinear features are extracted from the sub-bands. The nonlinear features used are based on fractal dimensions (FDs) and entropy theories. In the classification step, different approaches based on conventional machine learning (ML) and deep learning (DL) are discussed. In this step, a CNN–RNN-based DL method with the number of layers proposed is applied. The extracted features have been fed to the input of the proposed CNN–RNN model, and satisfactory results have been reported. In the classification step, the K-fold cross-validation with k = 10 is employed to demonstrate the effectiveness of the proposed CNN–RNN classification procedure. The results revealed that the proposed CNN–RNN method for Bonn and Freiburg datasets achieved an accuracy of 99.71% and 99.13%, respectively.
format	article
author	Anis Malekzadeh Assef Zare Mahdi Yaghoobi Hamid-Reza Kobravi Roohallah Alizadehsani
author_facet	Anis Malekzadeh Assef Zare Mahdi Yaghoobi Hamid-Reza Kobravi Roohallah Alizadehsani
author_sort	Anis Malekzadeh
title	Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features
title_short	Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features
title_full	Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features
title_fullStr	Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features
title_full_unstemmed	Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features
title_sort	epileptic seizures detection in eeg signals using fusion handcrafted and deep learning features
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/898912f40c244380b138ec817be2383f
work_keys_str_mv	AT anismalekzadeh epilepticseizuresdetectionineegsignalsusingfusionhandcraftedanddeeplearningfeatures AT assefzare epilepticseizuresdetectionineegsignalsusingfusionhandcraftedanddeeplearningfeatures AT mahdiyaghoobi epilepticseizuresdetectionineegsignalsusingfusionhandcraftedanddeeplearningfeatures AT hamidrezakobravi epilepticseizuresdetectionineegsignalsusingfusionhandcraftedanddeeplearningfeatures AT roohallahalizadehsani epilepticseizuresdetectionineegsignalsusingfusionhandcraftedanddeeplearningfeatures
_version_	1718410474671833088

Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features

Ejemplares similares