Forecasting Epileptic Seizures Using XGBoost Methodology and EEG Signals

Sunkara Mounika; Reeja S R

doi:10.4108/eetpht.10.5569

Authors

Sunkara Mounika Vellore Institute of Technology University
Reeja S R Vellore Institute of Technology University

DOI:

https://doi.org/10.4108/eetpht.10.5569

Keywords:

Machine Learning, Epileptic Seizures XGBoost, Electroencephalogram, SMOTE

Abstract

INTRODUCTION: Epilepsy denotes a disorder of neurological origin marked by repetitive and spontaneous seizures without any apparent trigger. Seizures occur due to abrupt and heightened electricity flowing through the brain, which can lead to physical and mental symptoms. There are several types of epileptic seizures, and epilepsy itself can be caused by various underlying conditions. EEG (Electroencephalogram) is one of the most important and widely used tools for epileptic seizure prediction and diagnosis. EEG uses skull sensors to record electrical signals from the brain., and it can provide valuable insights into brain activity patterns associated with seizures.

OBJECTIVES: Brain-computer interface technology pathway for analyzing the EEG signals for seizure prediction to eliminate the class imbalance issue from our dataset in this case, a SMOTE approach is applied. It is observable that there are more classes of one variable than there are of the others in the output variable. This will be problematic when employing different Artificial intelligence techniques since these algorithms are more likely to be biased towards a certain variable because of its high prevalence

METHODS: SMOTE approaches will be used to address this bias and balance the number of variables in the response variable. To develop an XGBoost (Extreme Gradient Boosting) model using SMOTE techniques to increase classification accuracy.

RESULTS: The results show that the XGBoost method achieves a 98.7% accuracy rate.

CONCLUSION: EEG-based model for seizure type using the XGBoost model for predicting the disease early. The Suggested method could significantly reduce the amount of time needed to accomplish seizure prediction.

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