TY - GEN
T1 - Epilepsy Detection using Combination DWT and Convolutional Neural Networks Based on Electroencephalogram
AU - Sunaryono, Dwi
AU - Siswantoro, Joko
AU - Sarno, Riyanarto
AU - Susilo, Rahardian Indarto
AU - Sabilla, Shoffi Izza
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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%.
AB - 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%.
KW - Convolutional Neural Network
KW - Discrete Wavelet Transform
KW - EEG
KW - Epilepsy
UR - http://www.scopus.com/inward/record.url?scp=85171165491&partnerID=8YFLogxK
U2 - 10.1109/ISITIA59021.2023.10221031
DO - 10.1109/ISITIA59021.2023.10221031
M3 - Conference contribution
AN - SCOPUS:85171165491
T3 - 2023 International Seminar on Intelligent Technology and Its Applications: Leveraging Intelligent Systems to Achieve Sustainable Development Goals, ISITIA 2023 - Proceeding
SP - 411
EP - 416
BT - 2023 International Seminar on Intelligent Technology and Its Applications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 24th International Seminar on Intelligent Technology and Its Applications, ISITIA 2023
Y2 - 26 July 2023 through 27 July 2023
ER -