Epilepsy EEG Seizure Prediction Based on the Combination of Graph Convolutional Neural Network Combined with Long- and Short-Term Memory Cell Network
With the increasing research of deep learning in the EEG field, it becomes more and more important to fully extract the characteristics of EEG signals. Traditional EEG signal classification prediction neither considers the topological structure between the electrodes of the signal collection device...
Saved in:
| Main Authors: | Zhejun Kuang, Simin Liu, Jian Zhao, Liu Wang, Yunkai Li |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/24/11569 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Using Long Short-Term Memory (LSTM) recurrent neural networks to classify unprocessed EEG for seizure prediction
by: Jordan D. Chambers, et al.
Published: (2024-11-01) -
Incidence and Predictors of Later Epilepsy in Neonates with Encephalopathy: The Impact of Electrographic Seizures
by: Carol M. Stephens, et al.
Published: (2025-02-01) -
Seizure Onset Zone Detection Based on Convolutional Neural Networks and EEG Signals
by: Zhejun Kuang, et al.
Published: (2024-10-01) -
Resting state EEG in young children with Tuberous Sclerosis Complex: associations with medications and seizures
by: Caitlin C. Clements, et al.
Published: (2025-01-01) -
Predicting Patient-Specific Epileptic Seizures From Scalp EEG Signals Using KNN Model With Transfer Learning
by: T. Premavathi, et al.
Published: (2024-01-01)