E3S Web Conf.
Volume 351, 202210th International Conference on Innovation, Modern Applied Science & Environmental Studies (ICIES’2022)
|Number of page(s)||6|
|Published online||24 May 2022|
Deep Learning for Epilepsy monitoring: A survey
1 Laboratory of Artificial Intelligence, Data Science and Emerging Systems, National School of Applied Sciences of Fez, USMBA Fez, Morocco
2 ISIC Research Team of High School of Technology, 2ISEI Laboratory, Moulay Ismail University, Meknes, Morocco
3 Clinical Neurosciences Laboratory, Faculty of Medicine and Pharmacy of Fez, USMBA Fez, Morocco
4 Neurology Department, Sleep center Hassan II University Hospital, USMBA Fez, Morocco
* Corresponding author: Ghita.firstname.lastname@example.org
Diagnosis of epilepsy can be expensive, time-consuming, and often inaccurate. The gold standard diagnostic monitoring is continuous video-electroencephalography (EEG), which ideally captures all epileptic events and dis-charges. Automated monitoring of seizures and epileptic activity from EEG would save time and resources, it is the focus of much EEG-based epilepsy research. The purpose of this paper is to provide a survey in order to understand, classify and benchmark the key parameters of deep learning-based approaches that were applied in the processing of EEG signals for epilepsy monitoring. This survey identifies the availability of data and the black-box nature of DL as the main challenges hindering the clinical acceptance of EEG analysis systems based on Deep Learning and suggests the use of Explainable Artificial Intelligence (XAI) and Transfer Learning to overcome these issues. It also underlines the need for more research to recognize the full potential of big data, Computing Edge, IoT to implement wearable devices that can assist epileptic patients and improve their quality of life.
Key words: Epilepsy / Deep learning / Electroencephalography / EEG signal analysis
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.