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

J. Andrew Zhang; Alex He-Mo; R. Yin; Chang Li; Yuzhi Tang; Dharmendra Gurve; Veronique van der Horst; Aron S Buchman; Bo Wang; Maged Goubran; Bo Wang; Andrew S P Lim

doi:10.1093/sleep/zsag022

February 6, 2026

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

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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.

Implication

A deep learning model infers major sleep stages in adults using a wearable system, indicating a new sleep monitoring approach.

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/698828410fc35cd7a8847902 https://doi.org/10.1093/sleep/zsag022

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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