Supervised Machine Learning and Deep Learning Techniques for Epileptic Seizure Recognition Using EEG Signals-A...

Area of Science:

• * Neurology
• * Biomedical Engineering
• * Data Science

Background:

• * Electroencephalography (EEG) signals are complex and non-stationary, requiring advanced analysis.
• * Conventional machine learning (ML) methods necessitate extensive preprocessing and feature extraction for EEG analysis.
• * Deep learning (DL) offers autonomous feature learning from raw EEG data, showing potential for improved analysis.

Purpose of the Study:

• * To provide a comprehensive overview of challenges in EEG-based seizure detection, prediction, and classification.
• * To systematically review and compare the efficacy of ML and DL methodologies in analyzing EEG for epilepsy.
• * To identify current trends and future directions in applying ML and DL to enhance the lives of epilepsy patients.

Main Methods:

• * A systematic literature review was conducted, searching Web of Science and Scopus databases.
• * Publications from 2017 to July 2022 were analyzed, yielding 91 eligible papers after rigorous screening.
• * Methodologies focused on machine learning and deep learning approaches for EEG seizure analysis.

Main Results:

• * Deep learning methods demonstrate significant potential in autonomously learning relevant features from raw EEG data.
• * The comparative advantage of DL over conventional ML for EEG seizure analysis remains an active area of investigation.
• * A variety of ML and DL approaches are being explored for different types and stages of epileptic seizures.

Conclusions:

• * Both ML and DL offer valuable tools for analyzing complex EEG signals in epilepsy.
• * Further research is needed to fully elucidate the advantages of DL over ML for clinical seizure detection and prediction.
• * Advancements in ML and DL applied to EEG analysis hold promise for improving patient outcomes and aiding clinical experts.