Paper Contents
Abstract
Neurological disorders, especially epilepsy, often manifest through unpredictable seizures that can severely impact patient safety and quality of life. Reliable seizure prediction has the potential to revolutionize clinical care by enabling timely interventions, improving patient independence, and reducing morbidity. This project explores the application of machine learning (ML) models for seizure prediction using electroencephalogram (EEG) signals and patient health data.The system leverages Python-based ML libraries to process pre-ictal, ictal, and post-ictal EEG signals. Various classifiers, including Random Forest, Support Vector Machines (SVM), and Long Short-Term Memory (LSTM) neural networks, are implemented to detect seizure patterns and provide real-time predictions. Experimental results demonstrate that the LSTM model achieves the highest accuracy of 98.5%, outperforming traditional models, due to its ability to capture temporal dependencies in EEG signals.This work contributes to the field of biomedical signal processing by presenting a scalable and adaptable seizure prediction framework. It highlights the role of ML in healthcare and provides directions for future research, such as real-time IoT integration, cloud-based deployment, and explainable AI for medical decision support.
Copyright
Copyright © 2025 Mohith c. This is an open access article distributed under the Creative Commons Attribution License.
