A review of epilepsy detection and prediction methods based on EEG signal processing and deep learning
Epilepsy is a chronic neurological disorder that poses significant challenges to patients and their families. Effective detection and prediction of epilepsy can facilitate patient recovery, reduce family burden, and streamline healthcare processes. Therefore, it is essential to propose a deep learni...
Saved in:
Main Authors: | Xizhen Zhang, Xiaoli Zhang, Qiong Huang, Fuming Chen |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2024-11-01
|
Series: | Frontiers in Neuroscience |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2024.1468967/full |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A Novel Real-Time Threshold Algorithm for Closed-Loop Epilepsy Detection and Stimulation System
by: Liang-Hung Wang, et al.
Published: (2024-12-01) -
Enhancing Epilepsy Seizure Detection Through Advanced EEG Preprocessing Techniques and Peak-to-Peak Amplitude Fluctuation Analysis
by: Muawiyah A. Bahhah, et al.
Published: (2024-11-01) -
Methods for Identifying Epilepsy Surgery Targets Using Invasive EEG: A Systematic Review
by: Karla Ivankovic, et al.
Published: (2024-11-01) -
Epilepsy Prediction and Detection Using Attention-CssCDBN with Dual-Task Learning
by: Weizheng Qiao, et al.
Published: (2024-12-01) -
Interpretation of routine and sleep EEG: Minimum recording standards of the International Federation of Clinical Neurophysiology and the International League Against Epilepsy (2023)
by: YU Min, ZHU Jiang, WANG Xiaoli, ZHANG Wenjuan, LIU Yonghong
Published: (2024-12-01)