Enhanced Salp Swarm Algorithm Based On Convolutional Neural Network Optimization For Automatic Epilepsy Detection

Sunaryono, Dwi and Sarno, Riyanarto and Siswantoro, Joko and Raharjo, Agus Budi and Sabilla, Shoffi Izza and Susilo, Rahadian Indarto and Rekha, Kana (2022) Enhanced Salp Swarm Algorithm Based On Convolutional Neural Network Optimization For Automatic Epilepsy Detection. Journal of Theoretical and Applied Information Technology, 100 (19). pp. 5615-5623. ISSN 1817-3195

	PDF Joko Siswantoro_Enhanced Salp Swarm Algorithm.pdf Download (2MB)
	PDF Bukti corresponding author JATIT 2022.pdf Download (222kB)

Abstract

Epilepsy is a neurological disorder that occurs due to abnormal activity in the brain. Symptoms can vary, such as uncontrolled movements, muscle stiffness, difficulty breathing, loss of consciousness, and even death. Therefore, the multichannel electroencephalogram (EEG) is very important to understand the pattern of seizure occurrence and non-seizure in epilepsy. In this paper, we determine an automatic epilepsy detection method using enhanced Salp Swarm Algorithm (SSA) CNN-based of EEG signals. The signal is transformed into Low Pass Filter (LPF) and High Pass Filter (HPF) with one level, frequencies, and scales using Wavelet Transform. Enhanced SSA was used to determine the number of neurons and the appropriate number of convolution layers in the CNN algorithm for classifying two classes (epilepsy and epilepsy with seizure) using the CHB-MIT dataset from Children's Hospital Boston. The results of the study show that the proposed method produces the highest accuracy of 99.15% and 89.04% of average accuracy. This result is obtained with a computation time on testing data of 0.0001 seconds using a high-end computer. Enhanced SSA was proven to increase the performance of CNN of 81.13%. The proposed method can be used in the automatic detection of epilepsy.

Item Type:	Article
Uncontrolled Keywords:	Epilepsy, CHB-MIT, Wavelet Transform, Convolutional Neural Network, Salp Swarm Algorithm
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Faculty of Engineering > Department of Informatic
Depositing User:	Joko Siswantoro
Date Deposited:	17 Oct 2022 01:52
Last Modified:	08 Nov 2022 10:34
URI:	http://repository.ubaya.ac.id/id/eprint/42749

Actions (login required)

View Item