Multimodal physiological signal learning for clinical sleep respiratory event recognition.

Authors

He Qin, Xiaocun Chen, Yao Liu, Xu Wei, Fei Chen, Lijie Lu, Liyun Liu, Tanjun Wei, Xionghui Hu, Xianhai Li, Xinhui Cheng

Published in

Frontiers in neurology. Volume 17. Pages 1929036. Epub Sep 08, 2026.

Abstract

Automatic recognition of sleep-related breathing events is of great significance for auxiliary sleep disorder screening, clinical interpretation, and long-term physiological monitoring. To address the limitations of existing methods in complementary modeling of multimodal physiological signals, event-related feature aggregation, and imbalanced class learning, this study proposes a multimodal state-space representation learning framework for sleep-related breathing event recognition. The framework takes the neurophysiological signal feature matrix and the respiration-acoustic-related feature matrix as inputs and employs a dual-branch state-space encoder to separately extract sequential dynamic features from different modalities. Furthermore, a cross-state token routing module is designed to realize fine-grained inter-modal information exchange, while an event query prototype fusion module aggregates event-related discriminative features from multimodal sequential representations. During training, class-balanced focal margin loss, sleep-stage auxiliary supervision, and arousal-state auxiliary supervision are jointly employed to improve the model's adaptability to complex sleep segments and imbalanced event distributions. Under patient-level five-fold cross-validation, the proposed method achieves an Accuracy of 88.19%, a Precision of 84.51%, a Recall of 86.28%, and an AUROC of 92.71% on the PSG-Audio public dataset. On the clinical dataset, it achieves an Accuracy of 74.33%, a Precision of 77.51%, a Recall of 84.33%, and an AUROC of 90.51%. These results indicate favorable overall performance of the proposed framework and provide supporting evidence for its ability to learn complementary multimodal physiological representations for sleep-related breathing event recognition.

PMID:
42774037
Bibliographic data and abstract were imported from PubMed on 23 Sep 2026.

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