A Model for Epileptic Seizure Diagnosis Using the Combination of Ensemble Learning and Deep Learning

The primary objective of this project is to develop a highly accurate and reliable model for the diagnosis of epileptic seizures by utilizing a hybrid approach that combines the strengths of both ensemble learning and deep learning techniques. This model aims to harness the predictive power of algorithms like XGBoost (XGB), Support Vector Machines (SVM), Random Forest (RF), and Bidirectional Long Short-Term Memory (BiLSTM) to analyze EEG signals.