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February 8, 20261 citations

Mamba-based Deep Learning Approach for Sleep Staging on a Wireless Multimodal Wearable System Without Electroencephalography.

JZJ. Andrew ZhangMoorpark CollegeAHAlex He-MoSunnybrook Health Science CentreRYR. YinSunnybrook Health Science Centre

Key Points

  • This research aims to develop a deep learning model to accurately identify sleep stages using a wearable device without EEG.
  • Developed a Mamba-based deep learning model.
  • Applied model to data from a wireless multimodal wearable system.
  • Tested effectiveness on data from adults in a sleep clinic.
  • Successfully inferred major sleep stages from the wearable system.
  • Eliminated the need for electroencephalography in sleep monitoring.

Abstract

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

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Cite This Study

Zhang et al. (2026) studied this question.

synapsesocial.com/papers/698828410fc35cd7a8847902https://doi.org/10.1093/sleep/zsag022
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