Identification of Epilepsy from EEG Signal using Recurrent Neural Network


  • Nurul Izyan Ainaa Jumari Universiti Tun Hussein Onn Malaysia
  • Ashok Vajravellu


EEG, epilepsy, RNN, LSTM, ANN


The implementation of a Deep Neural Network (DNN) is widely used for a long time ago. Nowadays, the DNN still undergoes improvements in a lot of research for different subjects including epilepsy. This disorder still remains one of the diagnoses that can be done by using DNN. For this study, one of the DNN models which is the Recurrent Neural Network (RNN) is utilized to make identification for epilepsy based on the Electroencephalography (EEG) dataset. The dataset is attained from UCI Machine Learning Repository for Epileptic Seizure Recognition Data which is an open-source dataset for epilepsy. The dataset with epileptic can be distinguished from the non-epileptic one by using the LSTM model, the upgrade version of RNN. The performance evaluation in terms of accuracy also obtained a value of more than 98%.




How to Cite

Jumari, N. I. A., & Vajravellu, A. (2022). Identification of Epilepsy from EEG Signal using Recurrent Neural Network. Evolution in Electrical and Electronic Engineering, 3(2), 844–855. Retrieved from



Biomedical Engineering