Examining Various Deep Learning Techniques Employed in Predicting Seizures and Their Effectiveness

EasyChair Preprint 14141

Abstract

Seizure prediction holds significant promise in improving the quality of life for individuals with epilepsy by enabling timely interventions and better management strategies. Deep learning techniques have emerged as powerful tools in this domain, offering the potential to enhance the accuracy and efficiency of seizure prediction systems. This examination delves into various deep learning methodologies, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), and Deep Belief Networks (DBNs), exploring their application in predicting seizures. Through a comprehensive review of these techniques, their effectiveness, limitations, and comparative analysis are scrutinized. Additionally, the study investigates data preprocessing techniques, evaluation metrics, and performance analysis to provide insights into the current landscape of deep learning in seizure prediction. By addressing challenges and outlining future research directions, this examination aims to contribute to the advancement of seizure prediction methodologies and their practical implications in healthcare settings.

Keyphrases: Convolutional Neural Networks (CNNs), Deep Learning Models, EEG data, Gated Recurrent Unit (GRU) networks, Long Short-Term Memory (LSTM) networks, Recurrent Neural Networks (RNNs), seizure prediction

@booklet{EasyChair:14141,
  author    = {Samon Daniel},
  title     = {Examining Various Deep Learning Techniques Employed in Predicting Seizures and Their Effectiveness},
  howpublished = {EasyChair Preprint 14141},
  year      = {EasyChair, 2024}}