Abstract
Sleep stage classification is crucial in diagnosing sleep disorders and monitoring treatment effectiveness, yet it is inconvenient, requiring many electrodes and labor-intensive assessments. Single electroencephalogram (EEG)-based deep learning approaches were proposed to address this issue; however, existing studies have employed deep learning models that are devised for imaging and natural language processing for automated sleep stage classification, which limits their use for capturing sleep patterns or changes over time in EEG signals. This study proposes SleepExpertNet, which is inspired by actual clinical guidelines for scoring sleep stages. The model consists of a representation learning part, which learns the physiological characteristics of EEG sleep signals, and a temporal context modeling part, which learns long- and short-term temporal context information similar to a sleep expert. SleepExpertNet guarantees the highest level of accuracy (accuracy: 90.8%, macro-f1: 86.7) on the expanded Sleep-EDF dataset, surpassing the existing state-of-the-art methods, despite utilizing only single-channel EEG signals. Furthermore, the class imbalance problem, which has been a major obstacle in sleep stage classification research, was also addressed. The proposed model is expected to reduce the workload on clinicians and support diagnostic decision-making, enabling more accurate sleep stage classification.
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The datasets utilized in this study were downloaded from publicly available sources and were not modified. Datasets may be accessed according to the references and the following URL: https://www.physionet.org/content/sleep-edfx/1.0.0/.
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Change history
08 November 2022
A Correction to this paper has been published: https://doi.org/10.1007/s12652-022-04476-7
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Funding
This work was supported by the Korea Medical Device Development Fund Grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health and Welfare, the Ministry of Food and Drug Safety) (Project No. 1711139120, KMDF_PR_20210528_0001); by a National Research Foundation of Korea (NRF) Grant funded by the Korean government (Ministry of Science and ICT, MSIT) (No. 2022R1A2C1013205); by Institute for Information and Communications Technology Promotion (IITP) Grant funded by the Korea government (MSIT) (No. 2017-0-00432, Development of non-invasive integrated BCI SW platform to control home appliances and external devices by user's thought via AR/VR interface); by the Korea Health Technology R&D project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health and Welfare (grant number: HI22C0946); by a National Research Foundation of Korea (NRF) Grant funded by the Korean government (Ministry of Science and ICT, MSIT) (No. 2020R1C1C1006773).
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CHL conceptualized the study design, developed the deep learning model, computational framework and draft the manuscript. HJK performed data analysis, critically reviewed, and revised the manuscript. YTK visualized the materials, critically reviewed, and revised the manuscript. HK and JBK critically reviewed and revised the manuscript. DJK conceived and designed the study, established the methodology, and oversaw the creation of the final manuscript. All authors read and approved the final manuscript.
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Lee, CH., Kim, HJ., Kim, YT. et al. SleepExpertNet: high-performance and class-balanced deep learning approach inspired from the expert neurologists for sleep stage classification. J Ambient Intell Human Comput 14, 8067–8083 (2023). https://doi.org/10.1007/s12652-022-04443-2
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DOI: https://doi.org/10.1007/s12652-022-04443-2