A Self-Interpretable Deep Learning Model for Seizure Prediction Using a Multi-Scale Prototypical Part Network
The epileptic seizure prediction (ESP) method aims to timely forecast the occurrence of seizures, which is crucial to improving patients’ quality of life. Many deep learning-based methods have been developed to tackle this issue and achieve significant progress in recent years. However, t...
Saved in:
| Main Authors: | Yikai Gao, Aiping Liu, Lanlan Wang, Ruobing Qian, Xun Chen |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10078917/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Amplitude-integrated electroencephalography for neonatal seizure detection. An electrophysiological point of view
by: Sebastián Gacio -
A companion to the preclinical common data elements for phenotyping seizures and epilepsy in rodent models. A report of the TASK3‐WG1C: Phenotyping working group of the ILAE/AES joint translational task force
by: Melissa Barker‐Haliski, et al.
Published: (2025-08-01) -
VARIANTS OF THE GENERALIZATION OF EPILEPTIC SEIZURES IN MESIAL TEMPORAL EPILEPSY IN ADULTS
by: V. О. Generalov, et al.
Published: (2016-08-01) -
Bilateral tonic-clonic seizures and status epilepticus in autoimmune encephalitis
by: M. Yu. Maximova, et al.
Published: (2025-05-01) -
Latency of epileptic and psychogenic nonepileptic seizures
by: Hulya Ozkan, et al.
Published: (2023-07-01)