Epilepsy Prediction using a Combined LSTM – XGBoost System on EEG Signals
Shanmuga Skandh Vinayak E1, Shahina A2, Nayeemulla Khan A3

1Shanmuga Skandh Vinayak E*, Department of Information Technology, Sri Sivasubramaniya Nadar College of Engineering, Chennai, India.
2Shahina A, Department of Information Technology, Sri Sivasubramaniya Nadar College of Engineering, Chennai, India.
3Nayeemulla Khan A, School of Computing Sciences and Engineering, Vellore Institute of Technology, Chennai, India.

Manuscript received on September 22, 2020. | Revised Manuscript received on November 03, 2020. | Manuscript published on November 10, 2021. | PP: 18-24 | Volume-10 Issue-1, November 2020 | Retrieval Number: 100.1/ijitee.A80861110120| DOI: 10.35940/ijitee.A8086.1110120
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Ranking 4th in the list of most common neurological diseases, Epilepsy – a severe chronic disorder that causes recurrent and unprovoked seizures, affects over 1% of the world population. One of the most preliminary and commonly used mechanisms to test the presence of epilepsy in patients is the electroencephalogram (EEG). EEG – an instrument capable of recording the electrical activity in the brain. The EEG data are capable of revealing information, unique to a patient with episodes of seizure. In this article a system capable of detecting such information is proposed, using neural networks and machine learning algorithms, which can be utilized in the automation process of epilepsy detection. The proposed system utilizes the Long Short-Term Memory (LSTM) neural network algorithm and the eXtreme Gradient Boosting (XGB) algorithm, to classify the channels of the EEG data. The system produces an average accuracy of 96.2% in the LSTM channel classification models and an ensemble classification of the LSTM classifications using XGB, producing an average accuracy of 98.5%. Data encoding is employed in the system, which improves the efficiency and performance of the system by exhibiting a classification duration of 31s/sample. 
Keywords: Epilepsy, Detection, EEG, Sliding Window, Encoder, Neural Network, Machine Learning, LSTM, XGB.
Scope of the Article: Machine Learning