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Towards interpretable sleep stage classification with a multi-stream fusion network

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Publicado en:BMC Medical Informatics and Decision Making vol. 25 (2025), p. 1
Autor principal: Chen, Jingrui
Otros Autores: Fan, Xiaomao, Ge, Ruiquan, Xiao, Jing, Wang, Ruxin, Ma, Wenjun, Li, Ye
Publicado:
Springer Nature B.V.
Materias:
Sleep
Datasets
Classification
Convolution
Chebyshev approximation
Data integration
Electromyography
Electroencephalography
Monitoring
Fourier transforms
Natural language
Machine learning
Eye movements
Performance measurement
Computer vision
Sleep disorders
Neural networks
Support vector machines
Temporal variations
Algorithms
Decision trees
Acceso en línea:Citation/Abstract
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Resumen:Sleep stage classification is a significant measure in assessing sleep quality and diagnosing sleep disorders. Many researchers have investigated automatic sleep stage classification methods and achieved promising results. However, these methods ignored the heterogeneous information fusion of the spatial–temporal and spectral–temporal features among multiple-channel sleep monitoring signals. In this study, we propose an interpretable multi-stream fusion network, named MSF-SleepNet, for sleep stage classification. Specifically, we employ Chebyshev graph convolution and temporal convolution to obtain the spatial–temporal features from body-topological information of sleep monitoring signals. Meanwhile, we utilize a short time Fourier transform and gated recurrent unit to learn the spectral–temporal features from sleep monitoring signals. After fusing the spatial–temporal and spectral–temporal features, we use a contrastive learning scheme to enhance the differences in feature patterns of sleep monitoring signals across various sleep stages. Finally, LIME is employed to improve the interpretability of MSF-SleepNet. Experimental results on ISRUC-S1 and ISRUC-S3 datasets show that MSF-SleepNet achieves competitive results and is superior to its state-of-the-art counterparts on most of performance metrics.
ISSN:1472-6947
DOI:10.1186/s12911-025-02995-9
Fuente:Healthcare Administration Database