A Framework for Suggesting a New Methodology for Early Detection of Epilepsy Seizure

Epilepsy, a severe and chronic neurological condition, is detectable through the analysis of brain signals, typically monitored using Electroencephalogram (EEG) and Electrocardiography (ECG). Despite the complexity and noise associated with these signals, they are essential for diagnosing epilepsy, which accounts for approximately 1% of the global disease burden. The recurrence of seizures is a primary indicator of epilepsy, posing significant challenges for seizure identification and understanding brain-related phenomena. This study proposes a predictive analysis system for epilepsy seizure detection, empowering medical practitioners with timely insights for patient care. Leveraging an ensemble machine learning model combining SVM, Random Forest, and Logistic Regression, the system achieves early identification of seizure activity. Utilizing PhysioNet datasets for training, the model accurately predicts epilepsy seizures, enhancing diagnostic accuracy by integrating brain and heart signals, thus surpassing existing models. This research significantly contributes to advancing seizure diagnosis and treatment efficacy.