Exploring the Applicability of Transfer Learning and Feature Engineering in Epilepsy Prediction Using Hybrid Transformer Model

Exploring the Applicability of Transfer Learning and Feature Engineering in Epilepsy Prediction Using Hybrid Transformer Model

Objective: Epilepsy prediction algorithms offer patients with drug-resistant epilepsy a way to reduce unintended harm from sudden seizures. The purpose of this study is to investigate the applicability of transfer learning (TL) technique and model inputs for different deep learning (DL) model struct...

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Bibliographic Details
Main Authors: Shuaicong Hu, Jian Liu, Rui Yang, Ya'Nan Wang, Aiguo Wang, Kuanzheng Li, Wenxin Liu, Cuiwei Yang
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Transactions on Neural Systems and Rehabilitation Engineering
Subjects:
Epilepsy prediction
feature engineering
scalp electroencephalogram (sEEG)
hybrid transformer
transfer learning (TL)
Online Access:https://ieeexplore.ieee.org/document/10046136/

Internet

https://ieeexplore.ieee.org/document/10046136/

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