EpiNet: A Hybrid Machine Learning Model for Epileptic Seizure Prediction using EEG Signals from a 500 Patient Dataset

Abstract

The accurate prognosis of epileptic seizures has great significance in enhancing the management of epilepsy, necessitating the creation of robust and precise predictive models. EpiNet, our hybrid machine learning model for EEG signal analysis, incorporates key elements of computer vision and ma-chine learning, positioning it within this advancing technological domain for enhanced seizure prediction accuracy. Hence, this research aims to provide a thorough investigation using the Bonn Electroencephalogram (EEG) signals dataset as an alternative method. The methodology used in this study encompasses the training of five machine learning models, such as Support Vector Machines (SVM), Gaussian Naive Bayes, Gradient Boosting, XGBoost, and LightGBM. Performance criteria, including ac-curacy, sensitivity, specificity, precision, recall, and F1-score, are extensively used to assess the efficacy of each model. A unique contribution is the development of a hybrid model, integrating predictions from individual models to enhance the overall accu-racy of epilepsy identification. Experimental results demonstrate notable success, with the hybrid model achieving an accuracy of 99.81%. Performance matrices for both classes demonstrate the hybrid model’s epileptic seizure prediction reliability. Vi-sualizations, including ROC-AUC curves and accuracy curves, provide a nuanced understanding of the models’ discriminative abilities and performance improvement with increasing sample size. A comparative analysis with existing studies reaffirms the advancement of our research, positioning it at the forefront of epileptic seizure prediction. This study not only highlights the promising integration of machine learning in medical diagnostics but also emphasises areas for future refinement. The achieved results open avenues for proactive healthcare management and improved patient outcomes.