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All Journal Journal of Electrical, Electronic, Information, and Communication Technology (JEEICT)
Muhammad Alif Rizky Naufal
Electrical Engineering Department Sebelas Maret University, Surakarta
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Application of LSTM Algorithm to Assist Diagnosis of Epilepsy Based on Electroencephalogram (EEG) Signals Sutrisno Ibrahim; Kaleb Nathan Zebua; Faisal Rahutomo; Muhammad Alif Rizky Naufal
Journal of Electrical, Electronic, Information, and Communication Technology Vol 7, No 1 (2025): JOURNAL OF ELECTRICAL, ELECTRONIC, INFORMATION, AND COMMUNICATION TECHNOLOGY
Publisher : Universitas Sebelas Maret (UNS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jeeict.7.1.100360

Abstract

Epilepsy is a common disease that affects the brain's ability and has the potential to destroy the quality of life of sufferers. Diagnosis of epilepsy can be done by clinical testing and by using the electroencephalography (EEG) method. This research aims to apply artificial intelligence to improve the effectiveness and accuracy of EEG signal analysis. Epilepsy diagnosis is done automatically based on trained EEG signal files. This application can be done by applying the Long-Short Term Memory (LSTM) machine learning algorithm for recognizing patterns from brain signals that lead to epilepsy. The development was carried out using the EEG signal dataset from the University of Bonn which consists of 5 data sets. The detection process consists of the stages of data loading, augmentation, filtering, training, and classification. The developed system will be loaded into a GUI to facilitate users. The result of this research is a machine learning model with Long Short-Term Memory (LSTM) algorithm that has an accuracy rate of 91%, validation accuracy of 94% and loss of 0.2. Compared to other machine learning approaches such as SVM, KNN, and ANN, the proposed method achieves higher accuracy without the need for explicit feature extraction, highlighting its effectiveness in time-series signal classification. The model evaluation results show that this research is successful in assisting the detection of epilepsy using EEG signals with a high level of accuracy and efficiency.
Co-Authors Faisal Rahutomo Kaleb Nathan Zebua Sutrisno Ibrahim