Predicting Epileptic Seizures From EEG Signals Using Machine Learning.

Objective: To develop a machine learning-based model for predicting epileptic seizures by analyzing electroencephalogram (EEG) signals and classifying the interictal and preictal phases. Methods: The EEG signals were analyzed in both the time and frequency domains using advanced feature extraction techniques. Three different machine learning approaches were employed, with special focus on long short-term memory (LSTM) networks. Features were extracted and classified to differentiate between seizure and non-seizure phases. The proposed models were evaluated using the publicly available CHB-MIT EEG dataset. The study was conducted between September 2022 and September 2024. Results: Among the tested models, the LSTM-based approach using the full-feature extraction pipeline achieved the highest prediction accuracy of 97.73%. This performance demonstrates strong potential for real-time seizure forecasting. Conclusion: Machine learning, particularly LSTM models, can accurately predict epileptic seizures by analyzing EEG signal patterns. These findings support the development of automated seizure prediction systems that could significantly improve the quality of life for epilepsy patients.