Mamba-based deep learning approach for sleep staging on a wireless multimodal wearable system without electroencephalography.

Overview

1. Department of Computer Science, University of Toronto, Toronto, ON, Canada
2. Department of Medicine, Sunnybrook Research Institute, Toronto, ON, Canada
3. Department of Computer Science, Vector Institute for Artificial Intelligence, Toronto, ON, Canada
4. Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, United States
5. Department of Neurological Sciences, Rush University Medical Center, Boston, MA, United States
6. Department of Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
7. Dept. of Medical Biophysics, University of Toronto, Toronto, ON, Canada
8. Department of Electrical and Computer Engineering, Queen's University, Kingston, ON, Canada
9. Dept. of Computer Science, University of Toronto, Toronto, ON, Canada
10. Department of Laboratory Medicine, University Health Network, Toronto, ON, Canada
11. Dept. of Medicine, University of Toronto, Toronto, ON, Canada
12. Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada

Abstract

Study Objectives: We investigate a Mamba-based deep learning approach for sleep staging on signals from ANNE One (Sibel Health, Chicago, IL), a non-intrusive dual-module wireless wearable system measuring chest electrocardiography, triaxial accelerometry, chest temperature, and finger photoplethysmography and finger temperature.

Methods: We obtained wearable sensor recordings from 357 adults undergoing concurrent polysomnography at a tertiary care sleep lab. Each polysomnography recording was manually scored, and these annotations served as ground truth labels for training and evaluation of our models. Polysomnography and wearable sensor data were automatically aligned using their electrocardiography channels with manual confirmation by visual inspection. We trained a Mamba-based recurrent neural network architecture on these recordings. Ensembling of model variants with similar architectures was performed.

Results: After ensembling, the model attains a 3-class (wake, non-rapid eye movement sleep, rapid eye movement sleep) balanced accuracy of 84.02 per cent, F1 score of 84.23 per cent, Cohen’s κ of 72.89 per cent, and a Matthews correlation coefficient (MCC) score of 73.00 per cent; a 4-class (wake, light NREM [N1/N2], deep NREM [N3], REM) balanced accuracy of 75.30 per cent, F1 score of 74.10 per cent, Cohen’s κ of 61.51 per cent, and MCC score of 61.95 per cent; a 5-class (wake, N1, N2, N3, REM) balanced accuracy of 65.11 per cent, F1 score of 66.15 per cent, Cohen’s κ of 53.23 per cent, MCC score of 54.38 per cent.

Conclusions: Our Mamba-based deep learning model can successfully infer major sleep stages from the ANNE One, a wearable system without electroencephalography, and can be applied to data from adults attending a tertiary care sleep clinic.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

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The data and code underlying this article will be shared on reasonable request to the corresponding author.

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Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 3 keywords, 15 MeSH terms, 1 funder, 32 references.

Cite

This paper

Zhang, A. H., He-Mo, A., Yin, R. F., Li, C., Tang, Y., Gurve, D., van der Horst, V., Buchman, A. S., Ghahjaverestan, N. M., Goubran, M., Wang, B., & Lim, A. S. P. (2026). Mamba-based deep learning approach for sleep staging on a wireless multimodal wearable system without electroencephalography. Sleep, 49(4), zsag022. https://doi.org/10.1093/sleep/zsag022

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