Classification of Epilepsy Diagnostic Results through EEG Signals Using the Convolutional Neural Network Method

Tri Kurnia Sari; Dian Palupi Rini; Samsuryadi Samsuryadi

doi:10.18495/comengapp.v12i2.429

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Affiliations

Tri Kurnia Sari
Universitas Sriwijaya

Dian Palupi Rini
Universitas Sriwijaya

Samsuryadi Samsuryadi
Universitas Sriwijaya

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Classification of Epilepsy Diagnostic Results through EEG Signals Using the Convolutional Neural Network Method

Tri Kurnia Sari ,
Dian Palupi Rini ,
Samsuryadi Samsuryadi

Vol 12 No 2 (2023)

DOI 10.18495/comengapp.v12i2.429

Submitted: Dec 20, 2022

Published: Jun 1, 2023

Abstract

The brain is one of the most important organs in the human body as a central nervous system which functions as a controlling center, intelligence, creativity, emotions, memories, and body movements. Epileptic seizure is one of the disorder of the brain central nervous system which has many symptoms, such as loss of awareness, unusual behavior and confusion. These symptoms lead in many cases to injuries due to falls, biting one’s tongue. Detecting a possible seizure beforehand is not an easy task. Most of the seizures occur unexpectedly, and finding ways to detect a possible seizure before it happens has been a challenging task for many researchers. Analyzing EEG signals can help us obtain information that can be used to diagnose normal brain activity or epilepsy. CNN has been demonstrated high performance on detection and classification epileptic seizure. This research uses CNN to classify the epilepsy EEG signal dataset. AlexNet and LeNet-5 are applied in CNN architecture. The result of this research is that the AlexNet architecture provides better precision, recall, and f1-score values on the epilepsy signal EEG data than the LeNet-5 architecture.