A Deep Learning Approach for Early Detection of Epileptic Seizures

Abstract

A Deep Learning Approach for Early Detection of Epileptic Seizures harnesses advanced deep learning techniques, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, to enable proactive epilepsy management through accurate, real-time seizure prediction. The CNN architecture, known for its powerful spatial feature extraction, identifies intricate EEG signal patterns, while LSTMs capture temporal dependencies, improving prediction precision across diverse seizure types. The integration of the Matching Pursuit algorithm enhances automatic feature extraction, minimizing manual intervention and increasing adaptability to various patient profiles. This hybrid model not only allows timely alerts and personalized treatment adjustments but also reduces false positives, facilitating continuous, non-invasive monitoring even outside clinical environments. Potential applications span critical care monitoring in ICUs, remote patient management via wearable devices, and neuro feedback systems, positioning this solution as a transformative advancement in epileptic care. By providing real-time, data-driven insights, this deep learning approach aims to enhance patient safety, optimize treatment protocols, and contribute to the growing body of knowledge in epilepsy research. Furthermore, the model's scalability and robustness make it a promising tool for integration into smart healthcare ecosystems, paving the way for improved quality of life for epilepsy patients worldwide.