Efficient Sleep Staging With Bayesian Uncertainty-Guided Active Learning

Automated sleep staging is essential for large-scale and home-based sleep monitoring; however, in routine clinical practice, sleep annotation remains largely dependent on experienced experts performing time-consuming and labor-intensive manual scoring. Existing automatic systems often struggle to adapt reliably to new subjects, limiting their clinical adoption and reinforcing the reliance on expert review. This creates a strong demand for adaptive and efficient sleep staging systems that can substantially reduce annotation workload while preserving expert-level accuracy. We propose BayesSleepNet, a novel framework that integrates Bayesian uncertainty quantification with active learning for adaptive sleep staging. BayesSleepNet employs principled Bayesian modeling by placing distributions over network weights and performing Monte Carlo sampling at inference, enabling explicit quantification of model (epistemic) uncertainty. These uncertainty estimates drive a two-stage sample selection strategy that first fine-tunes the model using representative epochs and subsequently prioritizes persistently uncertain samples for expert review. Across four public sleep datasets, BayesSleepNet consistently improves performance—by 7. 60% in accuracy, 8. 27% in macro-F1, and 0. 104 in Cohen's $\kappa$ —while requiring manual annotation of only 20% of data from new subjects. Despite its adaptive learning capability, BayesSleepNet remains computationally lightweight, using substantially fewer parameters than representative high-capacity state-of-the-art models. These results demonstrate the clinical promise of uncertainty-aware active learning as a practical and cost-efficient paradigm for semi-automated sleep staging. Code is available at https://github.com/yuty2009/bayesugal.

Authors

• Tianyou Yu
• Rui Huang University of Electronic Science and Technology of China
• Fei Wang
• Jun Zhang
• Wei Wu
• Zhuliang Yu Institute for Super Robotics (Huangpu) Shien-Ming Wu School of Intelligent Engineering, South China University of Technology School of Automation Science and Engineering, South China University of Technology
• Yuanqing Li
• Jun Xiao Zhejiang University
• Zhenghui Gu

Keywords

• Sleep
• Bayes methods
• Uncertainty
• Brain modeling
• Active learning
• Electroencephalography
• Annotations
• Reliability
• Adaptation models
• Predictive models
• Bayesian Inference
• Sleep Stages
• Monte Carlo Simulation
• Uncertainty Estimation
• Model Uncertainty
• Manual Annotation
• Expert Review
• Uncertainty Quantification
• Two-stage Strategy
• Epistemic Uncertainty
• Two-stage Selection
• Posterior Probability
• Bayesian Model
• Convolutional Layers
• Systematic Sampling
• EEG Signals
• Vanilla
• Predictive Distribution
• Bayesian Neural Network
• Macro F1 Score
• Prediction Uncertainty
• Standard Cross-validation
• Active Learning Strategies
• Sleep Architecture
• Number Of Experts
• Expert Annotations
• Variational Inference
• Stage N3
• Automatic sleep staging
• Bayesian neural networks