Epilepsy Detection using Combination DWT and Convolutional Neural Networks Based on Electroencephalogram

Sunaryono, Dwi and Siswantoro, Joko and Sarno, Riyanarto and Susilo, Rahadian Indarto and Sabilla, Shoffi Izza (2023) Epilepsy Detection using Combination DWT and Convolutional Neural Networks Based on Electroencephalogram. In: 2023 International Seminar on Intelligent Technology and Its Applications (ISITIA), 26-27 July 2023, Surabaya, Indonesia.

PDF Joko Siswantoro_Epilepsy Detection.pdf Download (1MB)

Abstract

At the present day, smart technology has made life simpler for people in all spheres of life, including medical. It is necessary to have technology that can identify diseases or physical defects in humans since this will influence the course of therapy. One of the cutting-edge technologies used to identify epilepsy is the electroencephalogram (EEG). The signal was obtained by observed brain’s electrical activity for a period of time to get these signals. Medical professionals need to be very accurate and confident in their ability to categorize EEG patterns in order to diagnose epilepsy. This study suggested using Zero Crossing Frequency and Mean Crossing Frequency features extracted from transformed singnal using Discrete Wavelet Transform. EEG signals were classified into three categories: ictal, pre-ictal, and normal using Convolutional Neural Network. According to the study's findings, the suggested approach can accurately categorize three categories with a confidence interval (CI) of 0.0013 and an accuracy of 98.09%.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	EEG, Discrete Wavelet Transform, Convolutional Neural Network, Epilepsy
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Faculty of Engineering > Department of Informatic
Depositing User:	Joko Siswantoro
Date Deposited:	20 Dec 2023 09:19
Last Modified:	31 Oct 2024 08:25
URI:	http://repository.ubaya.ac.id/id/eprint/45545

Actions (login required)

View Item