Unveiling Early Detection and Prevention of Seizures: Machine Learning and Deep Learning Approaches

EasyChair Preprint 14863

Abstract

Seizures, particularly in patients with epilepsy, present significant challenges in healthcare due to their unpredictable nature. Early detection and prevention are crucial for improving patient quality of life and mitigating the risks associated with sudden seizures. Recent advancements in machine learning (ML) and deep learning (DL) have opened new avenues for seizure prediction and prevention by analyzing vast amounts of physiological data, such as electroencephalogram (EEG) signals. This paper explores the current state of ML and DL approaches to seizure prediction, highlighting key algorithms, datasets, and signal processing techniques. Through the application of models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and support vector machines (SVMs), these techniques are increasingly capable of detecting seizure precursors from EEG signals with high accuracy. The integration of advanced feature extraction, data augmentation, and transfer learning further enhances predictive performance. This review also discusses the challenges faced in clinical implementation, including data variability, real-time processing, and generalizability of models across diverse populations. By comparing different approaches and their effectiveness, this paper provides a comprehensive overview of how machine learning and deep learning are revolutionizing seizure management, offering potential paths toward reliable early detection systems and personalized therapeutic interventions.

Keyphrases: Convolutional Neural Networks, Electroencephalogram, Epilepsy detection, deep learning, machine learning, seizure prediction

@booklet{EasyChair:14863,
  author    = {John Owen},
  title     = {Unveiling Early Detection and Prevention of Seizures: Machine Learning and Deep Learning Approaches},
  howpublished = {EasyChair Preprint 14863},
  year      = {EasyChair, 2024}}