TY - GEN
T1 - Exploring Feasibility of Truth-Involved Automatic Sleep Staging Combined with Transformer
AU - Yang, Ziwei
AU - Wang, Dong
AU - Chen, Zheng
AU - Huang, Ming
AU - Ono, Naoaki
AU - Altaf-Ul-Amin, Md
AU - Kanaya, Shigehiko
N1 - Funding Information:
This research and development work was supported by KAKENHI #21K12111 and the Grant-in-Aid for Early-Career Scientists #20K19923.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Recently, deep learning-based methods have been successfully proposed for electrophysiology signal-based sleep staging with promising results. Most existing methods use convolutional layers and recurrent-based architectures to implement a model structure from feature extraction to sequence signal classification. In this study, we propose a method of segmenting electroencephalogram (EEG) and electrooculogram (EOG) data according to frequency bands and construct a Transformer based automatic sleep classification model on top of it. The results show that the classifications of the stage Wake, N3, and REM outperform the state-of-art works, with the Fl-scores of 0.92, 0.85 and 0.91. Our work is the first attempt to explore the feasibility of a truth-involved Transformer-based model with a large-scale sleep database.
AB - Recently, deep learning-based methods have been successfully proposed for electrophysiology signal-based sleep staging with promising results. Most existing methods use convolutional layers and recurrent-based architectures to implement a model structure from feature extraction to sequence signal classification. In this study, we propose a method of segmenting electroencephalogram (EEG) and electrooculogram (EOG) data according to frequency bands and construct a Transformer based automatic sleep classification model on top of it. The results show that the classifications of the stage Wake, N3, and REM outperform the state-of-art works, with the Fl-scores of 0.92, 0.85 and 0.91. Our work is the first attempt to explore the feasibility of a truth-involved Transformer-based model with a large-scale sleep database.
UR - http://www.scopus.com/inward/record.url?scp=85125205213&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85125205213&partnerID=8YFLogxK
U2 - 10.1109/BIBM52615.2021.9669456
DO - 10.1109/BIBM52615.2021.9669456
M3 - Conference contribution
AN - SCOPUS:85125205213
T3 - Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
SP - 2920
EP - 2923
BT - Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
A2 - Huang, Yufei
A2 - Kurgan, Lukasz
A2 - Luo, Feng
A2 - Hu, Xiaohua Tony
A2 - Chen, Yidong
A2 - Dougherty, Edward
A2 - Kloczkowski, Andrzej
A2 - Li, Yaohang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Y2 - 9 December 2021 through 12 December 2021
ER -