Simplifying Multimodal With Single EOG Modality for Automatic Sleep Staging

Polysomnography (PSG) recordings have been widely used for sleep staging in clinics, containing multiple modality signals (i.e., EEG and EOG). Recently, many studies have combined EEG and EOG modalities for sleep staging, since they are the most and the second most powerful modality for sleep stagin...

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Bibliographic Details
Published in:IEEE transactions on neural systems and rehabilitation engineering 2024, Vol.32, p.1668-1678
Main Authors: Zhou, Yangxuan, Zhao, Sha, Wang, Jiquan, Jiang, Haiteng, Yu, Zhenghe, Li, Shijian, Li, Tao, Pan, Gang
Format: Article
Language:English
Subjects:
Adult
Algorithms
Brain modeling
Correlation
Datasets
EEG
Electroencephalography
Electroencephalography - methods
Electrooculography
Electrooculography - methods
Feature extraction
Female
Humans
Male
Multi modalities
Noise sensitivity
Polysomnography - methods
PSG recordings
Recording
Sleep
Sleep Stages - physiology
sleep staging
Young Adult
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Summary:Polysomnography (PSG) recordings have been widely used for sleep staging in clinics, containing multiple modality signals (i.e., EEG and EOG). Recently, many studies have combined EEG and EOG modalities for sleep staging, since they are the most and the second most powerful modality for sleep staging among PSG recordings, respectively. However, EEG is complex to collect and sensitive to environment noise or other body activities, imbedding its use in clinical practice. Comparatively, EOG is much more easily to be obtained. In order to make full use of the powerful ability of EEG and the easy collection of EOG, we propose a novel framework to simplify multimodal sleep staging with a single EOG modality. It still performs well with only EOG modality in the absence of the EEG. Specifically, we first model the correlation between EEG and EOG, and then based on the correlation we generate multimodal features with time and frequency guided generators by adopting the idea of generative adversarial learning. We collected a real-world sleep dataset containing 67 recordings and used other four public datasets for evaluation. Compared with other existing sleep staging methods, our framework performs the best when solely using the EOG modality. Moreover, under our framework, EOG provides a comparable performance to EEG.
ISSN:1534-4320
1558-0210
DOI:10.1109/TNSRE.2024.3389077