Towards Real-Time Sleep Stage Prediction and Online Calibration Based on Architecturally Switchable Deep Learning
IEEE Journal of Biomedical and Health Informatics
|October 25, 2023
Summary
A new real-time sleep staging network, SwSleepNet, accurately predicts sleep stages. This technology aids in sleep regulation and intervention for sleep disorders.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Artificial Intelligence
Background:
- Automatic sleep staging is crucial for sleep disorder intervention.
- Real-time sleep staging remains underexplored despite advances.
Purpose of the Study:
- To propose SwSleepNet, a novel network for both offline and real-time sleep staging.
- To enable precise sleep stage prediction and calibration for sleep regulation and intervention.
Main Methods:
- SwSleepNet integrates Sequence Broadening Module (SBM), Sequential CNN (SCNN), Squeeze and Excitation (SE) block, and Sequence Consolidation Module (SCM) for offline analysis.
- For online analysis, SCNN and SE predict sleep stages from short segments, with a calibration mechanism for accuracy.
Main Results:
- SwSleepNet achieved high offline accuracies (84.5%-86.7%) on multiple datasets, outperforming existing methods.
- Online sleep staging demonstrated robust accuracy (>80%) using short segments and a calibration mechanism.
Conclusions:
- SwSleepNet offers a robust and stable real-time sleep staging solution.
- This architecture is expected to advance sleep regulation and sleep disorder intervention.
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