Machine Learning for Epilepsy An Automated Pathway to Predict Seizures

Abstract

Epilepsy is a neurological disorder affecting approximately 70 million people worldwide, with 85% of cases occurring in developing countries. Characterized by recurrent, unprovoked seizures, epilepsy significantly impacts a person’s quality of life and can lead to premature mortality. Electroencephalography (EEG) plays a crucial role in detecting and analyzing epileptic seizures by capturing brain activity through voltage changes. Traditional seizure detection methods are retrospective, limiting proactive response measures. This project aims to develop a machine learning-based system for real-time epilepsy prediction using EEG data, enhancing early detection and patient safety. The system preprocesses uploaded EEG data by removing null values and extracting relevant features linked to seizure activity. A Support Vector Machine (SVM) algorithm is employed to compare extracted features with trained datasets, identifying patterns indicative of epilepsy. The system architecture includes modules for data preprocessing, feature extraction, classification, and result generation. It provides real-time monitoring, seizure prediction, and alerts to patients and healthcare professionals, reducing risks and improving medical decision-making. Implemented using Python with a web-based interface powered by HTML, CSS, and SQLite, the system ensures accessibility for neurologists, healthcare providers, and researchers. Functional requirements include accurate seizure detection with at least 90% accuracy, real-time data processing, and continuous monitoring, while non-functional requirements focus on system response time, user-friendly design, and accessibility. By leveraging machine learning for epilepsy prediction, this project aims to bridge the gap between medical research and practical healthcare solutions, offering a proactive approach to managing epilepsy and enhancing patient outcomes.

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