SleepExpertNet: high-performance and class-balanced deep learning approach inspired from the expert neurologists for sleep stage classification

Choel Hui Lee, Hyun Ji Kim, Young Tak Kim, Hakseung Kim, Jung Bin Kim, Dong Joo Kim

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Pages (from-to)8067-8083
Number of pages17
JournalJournal of Ambient Intelligence and Humanized Computing
Volume14
Issue number6
DOIs
Publication statusPublished - 2023 Jun

Bibliographical note

Funding Information:
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).

Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Keywords

  • Class imbalance problem
  • Deep learning
  • EEG
  • Human-inspired
  • Multi-head attention
  • PSG
  • Sleep stage classification

ASJC Scopus subject areas

  • General Computer Science

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