CRT: a Convolutional Recurrent Transformer for automatic sleep state detection

Nuruzzaman Nobel, S. M., Masfequier Rahman Swapno, S. M., Mohsin Kabir, M., Mridha, M. F., Dey, N. and Sherratt, S. ORCID: https://orcid.org/0000-0001-7899-4445 (2025) CRT: a Convolutional Recurrent Transformer for automatic sleep state detection. IEEE Journal of Biomedical and Health Informatics. ISSN 2168-2208 doi: 10.1109/JBHI.2025.3543028 (In Press)

Abstract/Summary

Sleep is a crucial period of rest necessary for optimal cognitive function, psychological well-being, and execution of everyday tasks. In the field of sleep healthcare, the primary objective is to identify and classify the various sleep states. Implementing sleep state detection in a system is problematic and essential for accurate diagnosis. Our study used an integrated framework to recognize sleep states. The dataset contained approximately eight lakh data points sorted into two groups: onset and wake-up. We successfully deployed a cutting-edge Convolutional Recurrent Transformer (CRT) model for sleep state detection. The training accuracy of our detection model was measured at 97.83%, a constant validation accuracy of 97.07%, and a testing measurement accuracy of 97.23%, were maintained. These scores indicate the model’s proficiency in precisely recognizing the sleep states. Our system’s detection capabilities demonstrate the ability to identify different sleep states, enhance the accuracy of diagnoses and increase healthcare outcomes in this specialized field.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/122044
Identification Number/DOI	10.1109/JBHI.2025.3543028
Refereed	Yes
Divisions	Life Sciences > School of Biological Sciences > Biomedical Sciences Life Sciences > School of Biological Sciences > Department of Bio-Engineering
Publisher	IEEE
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