TrustSleepNet: A Trustable Deep Multimodal Network for Sleep Stage Classification

Guanjie Huang, Fenglong Ma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Correctly classifying different sleep stages is a critical and prerequisite step in diagnosing sleep-related issues. In practice, the clinical experts must manually review the polysomnography (PSG) recordings to classify sleep stages. Such a procedure is time-consuming, laborious, and potentially prone to human subjective errors. Deep learning-based methods have been successfully adopted for automatically classifying sleep stages in recent years. However, they cannot simply say 'I do not know' when they are uncertain in their predictions, which may easily create significant risk in clinical applications, despite their good performance. To address this issue, we propose a deep model, named TrustSleepNet, which contains evidential learning and cross-modality attention modules. Evidential learning predicts the probability density of the classes, which can learn an uncertainty score and make the prediction trustable in real-world clinical applications. Cross-modality attention adaptively fuses multimodal PSG data by enhancing the significant ones and suppressing irrelevant ones. Experimental results demonstrate that TrustSleepNet outperforms state-of-the-art benchmark methods, and the uncertainty score makes the prediction more trustable and reliable.

Original languageEnglish (US)
Title of host publicationBHI-BSN 2022 - IEEE-EMBS International Conference on Biomedical and Health Informatics and IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks, Symposium Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665487917
DOIs
StatePublished - 2022
Event2022 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2022 - Ioannina, Greece
Duration: Sep 27 2022Sep 30 2022

Publication series

NameBHI-BSN 2022 - IEEE-EMBS International Conference on Biomedical and Health Informatics and IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks, Symposium Proceedings

Conference

Conference2022 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2022
Country/TerritoryGreece
CityIoannina
Period9/27/229/30/22

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management
  • Biomedical Engineering
  • Instrumentation

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