SleepMambaNet: a lightweight multimodal framework for sleep staging and sleep disorder screening with multicenter validation.

Authors

Chao Zhang, Queliang Wang, Yaping Liu, Xiaoliang Li, Fan Jiang, Shuyan Li, Weirong Cui, Jingjing Guo

Published in

Physiological measurement. Volume 47. Issue 10. Oct 01, 2026. Epub Oct 01, 2026.

Abstract

Objective.Manual sleep staging is labor-intensive, whereas many automated systems remain computationally demanding and are validated on a single dataset. This study aimed to develop a lightweight multimodal framework for automated sleep staging and exploratory sleep-disorder screening from polysomnographic (PSG) signals.Approach.SleepMambaNet combines efficient channel attention to integrate complementary information across PSG channels with bidirectional Mamba modules to model long-range temporal dependencies between sleep epochs. The framework was evaluated on the public ISRUC-S1 and ISRUC-S3 datasets and a multicenter clinical cohort of 185 PSG recordings.Main results.In subject-independent experiments, SleepMambaNet achieved accuracy, macro-F1, and Cohen's kappa of 0.852, 0.824, and 0.803 on ISRUC-S3; 0.830, 0.801, and 0.773 on ISRUC-S1; and 0.854, 0.819, and 0.806 on the combined public and clinical data, respectively. The external cross-dataset evaluation yielded accuracy, macro-F1, and kappa of 0.828, 0.812, and 0.779. For exploratory sleep-disorder screening, the model achieved accuracy of 0.892,F1 score of 0.887, sensitivity of 0.956, specificity of 0.842, and precision-recall area under the curve of 0.831.Significance.These results show that cross-channel physiological information and inter-epoch temporal context can be modeled effectively with only 0.47 million trainable parameters across heterogeneous public and clinical PSG data. The combination of computational efficiency and cross-dataset performance provides a practical basis for prospective evaluation of automated PSG decision support, while the binary screening output should not be interpreted as a disease-specific or standalone clinical diagnosis.

PMID:
42817663
Bibliographic data and abstract were imported from PubMed on 01 Oct 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.