SleepFC: Feature Pyramid and Cross-Scale Context Learning for Sleep Staging.
IEEE Trans Neural Syst Rehabil Eng
; 32: 2198-2208, 2024.
Article
in En
| MEDLINE
| ID: mdl-38805336
ABSTRACT
Automated sleep staging is essential to assess sleep quality and treat sleep disorders, so the issue of electroencephalography (EEG)-based sleep staging has gained extensive research interests. However, the following difficulties exist in this issue 1) how to effectively learn the intrinsic features of salient waves from single-channel EEG signals; 2) how to learn and capture the useful information of sleep stage transition rules; 3) how to address the class imbalance problem of sleep stages. To handle these problems in sleep staging, we propose a novel method named SleepFC. This method comprises convolutional feature pyramid network (CFPN), cross-scale temporal context learning (CSTCL), and class adaptive fine-tuning loss function (CAFTLF) based classification network. CFPN learns the multi-scale features from salient waves of EEG signals. CSTCL extracts the informative multi-scale transition rules between sleep stages. CAFTLF-based classification network handles the class imbalance problem. Extensive experiments on three public benchmark datasets demonstrate the superiority of SleepFC over the state-of-the-art approaches. Particularly, SleepFC has a significant performance advantage in recognizing the N1 sleep stage, which is challenging to distinguish.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Sleep Stages
/
Algorithms
/
Neural Networks, Computer
/
Electroencephalography
/
Machine Learning
Limits:
Humans
Language:
En
Journal:
IEEE Trans Neural Syst Rehabil Eng
Year:
2024
Document type:
Article