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Article

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

1
Department of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad 6518115743, Iran
2
Department of Electrical Engineering, Mashhad Branch, Islamic Azad University, Mashhad 9187147578, Iran
3
Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3216, Australia
*
Author to whom correspondence should be addressed.
Academic Editors: Yu-Dong Zhang, Juan Manuel Gorriz and Yuankai Huo
Sensors 2021, 21(22), 7710; https://doi.org/10.3390/s21227710
Received: 13 October 2021 / Revised: 15 November 2021 / Accepted: 15 November 2021 / Published: 19 November 2021
(This article belongs to the Special Issue Explainable AI in Medical Sensors)
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. View Full-Text
Keywords: epileptic seizures; EEG; diagnosis; TQWT; nonlinear features; CNN–RNN epileptic seizures; EEG; diagnosis; TQWT; nonlinear features; CNN–RNN
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MDPI and ACS Style

Malekzadeh, A.; Zare, A.; Yaghoobi, M.; Kobravi, H.-R.; Alizadehsani, R. Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features. Sensors 2021, 21, 7710. https://doi.org/10.3390/s21227710

AMA Style

Malekzadeh A, Zare A, Yaghoobi M, Kobravi H-R, Alizadehsani R. Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features. Sensors. 2021; 21(22):7710. https://doi.org/10.3390/s21227710

Chicago/Turabian Style

Malekzadeh, Anis, Assef Zare, Mahdi Yaghoobi, Hamid-Reza Kobravi, and Roohallah Alizadehsani. 2021. "Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features" Sensors 21, no. 22: 7710. https://doi.org/10.3390/s21227710

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