Challenges in Automating Seizure Prediction with Deep Learning's Crucial Role

EasyChair Preprint 14134

Abstract

Automating seizure prediction through deep learning presents significant challenges despite its potential to revolutionize epilepsy management. This abstract explores the hurdles encountered in this domain, including issues related to data quality, model complexity, real-time prediction requirements, generalization to new patients, and ethical considerations. Deep learning plays a crucial role in addressing these challenges by enabling automatic feature learning, providing model flexibility, and ensuring scalability. Strategies such as data augmentation, model explainability techniques, real-time processing optimization, personalized medicine approaches, and ethical frameworks are proposed to overcome these obstacles. Understanding and tackling these challenges are essential for the successful deployment of automated seizure prediction systems, paving the way for improved epilepsy care and patient outcomes.

Keyphrases: Artificial Intelligence, Automated seizure prediction, EEG data analysis, GPU acceleration, deep learning, parallel processing, personalized medicine

@booklet{EasyChair:14134,
  author    = {Samon Daniel and Godwin Olaoye},
  title     = {Challenges in Automating Seizure Prediction with Deep Learning's Crucial Role},
  howpublished = {EasyChair Preprint 14134},
  year      = {EasyChair, 2024}}