EEG-Based Sleep Stage Classification via Neural Architecture Search.
IEEE Trans Neural Syst Rehabil Eng
; PP2023 Jan 23.
Article
en En
| MEDLINE
| ID: mdl-37022068
ABSTRACT
With the improvement of quality of life, people are more and more concerned about the quality of sleep. The electroencephalogram (EEG)-based sleep stage classification is a good guide for sleep quality and sleep disorders. At this stage, most automatic staging neural networks are designed by human experts, and this process is time-consuming and laborious. In this paper, we propose a novel neural architecture search (NAS) framework based on bilevel optimization approximation for EEG-based sleep stage classification. The proposed NAS architecture mainly performs the architectural search through a bilevel optimization approximation, and the model is optimized by search space approximation and search space regularization with parameters shared among cells. Finally, we evaluated the performance of the model searched by NAS on the Sleep-EDF-20, Sleep-EDF-78 and SHHS datasets with an average accuracy of 82.7%, 80.0% and 81.9%, respectively. The experimental results show that the proposed NAS algorithm provides some reference for the subsequent automatic design of networks for sleep classification.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Aspecto:
Patient_preference
Idioma:
En
Revista:
IEEE Trans Neural Syst Rehabil Eng
Asunto de la revista:
ENGENHARIA BIOMEDICA
/
REABILITACAO
Año:
2023
Tipo del documento:
Article