AUTOMATED EEG BASED EPILEPTIC SEIZURE DETECTION AND PREDICTION

Abstract

Epilepsy is a neurological disorder characterized by recurrent seizures caused by abnormal electrical activity in the brain. Continuous monitoring of electroencephalogram (EEG) signals is essential for early detection and prediction of epileptic seizures, yet manual analysis of EEG recordings is timeconsuming and prone to human error. This research presents an automated EEG-based epileptic seizure detection and prediction system using advanced machine learning techniques. The proposed system integrates signal preprocessing, feature extraction, and deep learning classification to accurately identify seizure patterns from EEG signals. Initially, raw EEG signals undergo preprocessing steps including noise removal, normalization, and segmentation to enhance signal quality. A hybrid deep learning architecture combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks is employed to capture both spatial and temporal features of brainwave signals. CNN layers extract spatial patterns and local signal characteristics, while LSTM layers model long-term temporal dependencies across EEG sequences. This hybrid model enables accurate classification of normal, pre-ictal, and ictal brain states, allowing early prediction of potential seizure events. The system is trained and validated using labeled EEG datasets and optimized using techniques such as dropout, batch normalization, and adaptive learning algorithms to improve model generalization and prevent overfitting. The developed framework also includes a web-based interface that enables realtime EEG signal visualization, automated seizure alerts, and patient data management. Experimental results demonstrate high accuracy and reliability in seizure detection and prediction. The proposed approach can significantly assist neurologists by providing early warning of seizures and improving clinical decision-making in epilepsy management.