Implementasi Metode Recurrent Neural Network Untuk Prediksi Kejang Pada Penderita Epilepsi Berdasarkan Data Electroenephalogram

Raisya Febiyane; Yulison Harry Chrisnanto; Gunawan Abdillah

doi:10.30865/jurikom.v12i3.8656

Authors

Raisya Febiyane Universitas Jenderal Achmad Yani, Cimahi
Yulison Harry Chrisnanto Universitas Jenderal Achmad Yani, Cimahi
Gunawan Abdillah Universitas Jenderal Achmad Yani, Cimahi

DOI:

https://doi.org/10.30865/jurikom.v12i3.8656

Keywords:

Deep Learning, RNN, EEG, Seizure, Epilepsy

Abstract

Epilepsy is a chronic neurological disorder that causes patients to experience recurrent seizures. Seizures are one of the main symptoms of epilepsy, requiring medical treatment and close monitoring. A major challenge in epilepsy management is the difficulty in predicting when seizures will occur. Electroencephalogram (EEG) can detect seizures as it contains physiological information about brain neural activity. This study aims to predict seizures using a Recurrent Neural Network (RNN) method based on EEG data. Deep Learning is a branch of Machine Learning that uses artificial neural networks to solve problems involving large datasets. The data used in this research is the Epileptic Seizure Recognition dataset obtained from Kaggle. It consists of patient ID attributes, 178 numerical attributes representing EEG signals, and a label y indicating conditions during the recording, including eyes open, eyes closed, healthy brain, tumor location, and seizure activity. The deep learning model tested is a Recurrent Neural Network (RNN) designed to learn patterns in the data. Performance evaluation was conducted using metrics including accuracy, precision, recall, and F1-Score. Based on the application of the RNN method and testing using EEG data, the best condition was achieved with a three-layer Long Short-Term Memory architecture and optimal training parameters, resulting in a seizure prediction accuracy of 98.6%. This result demonstrates that the model is capable of effectively and efficiently predicting the likelihood of seizure occurrences.

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