ArticleSleep2026
Mamba-based deep learning approach for sleep staging on a wireless multimodal wearable system without electroencephalography.
Article in Sleep, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Wearable Sleep-Stage Classification: A Systematic Analysis of Signal Modalities, Representations, and Multimodal Fusion.Sensors (Basel, Switzerland) · 2026Article
- Sleep Stage Classification During CPAP Therapy from CPAP-Airflow and Wearable Fingertip Signals.Sensors (Basel, Switzerland) · 2026Article
- Transforming Sleep Monitoring: Review of Wearable and Remote Devices Advancing Home Polysomnography and Their Role in Predicting Neurological Disorders.Biosensors · 2025Review
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Authors and funding
12 authors.
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Abstract
STUDY
objectivesWe 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.
methodsWe 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.
resultsAfter 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.
conclusionsOur 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.
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