Hybrid One-Dimensional CNN and DNN Model for Classification Epileptic Seizure

Sunaryono, Dwi and Sarno, Riyanarto and Siswantoro, Joko and Purwitasari, Diana and Sabilla, Shoffi Izza and Susilo, Rahadian Indarto and Akbar, Naufal Rafi (2022) Hybrid One-Dimensional CNN and DNN Model for Classification Epileptic Seizure. International Journal of Intelligent Engineering and Systems, 15 (6). pp. 492-502. ISSN 2185-310X; E-ISSN 2185-3118

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Abstract

Epilepsy is a common chronic brain disease caused by abnormal neuronal activity and the occurrence of sudden or transient seizures. Electroencephalogram (EEG) is a non-invasive technique commonly used to identify epileptic brain activity. However, visual detection of the EEG is subjective, time consuming, and labour intensive for the neurologist. Therefore, we propose an automatic seizure detection using a combination of one-dimension convolution neural network (1D-CNN) with majority voting and deep neural network (DNN). EEG signals features are extracted using discrete Fourier transform (DFT) and discrete wavelet transform (DWT) which then these features will be selected with XGBoost to minimize features classified with CNN. The proposed method experimental results show that it can detect epilepsy from EEG signals perfectly with an accuracy of 100%. However, the proposed method only yielded classified EEG signals from the University of Bonn Dataset as its results.

Item Type:	Article
Uncontrolled Keywords:	Dnn,Eeg, signalsEpilepsy,One-dimensional cnn
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Faculty of Engineering > Department of Informatic
Depositing User:	Joko Siswantoro
Date Deposited:	07 Apr 2023 03:18
Last Modified:	31 Oct 2024 08:18
URI:	http://repository.ubaya.ac.id/id/eprint/43779

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