Temporally Continuous Automated Sleep-Wake Classification Using Deep Learning
Pranavan Somaskandhan1, Henri Korkalainen2,3, Timo Leppänen1,2,3
1School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, Australia.
Medrxiv : the Preprint Server for Health Sciences
|December 17, 2025
Summary
This study introduces a deep learning sleep-wake classifier that overcomes limitations of fixed 30-second epochs. The novel model provides high-temporal-resolution sleep scoring for more accurate physiological assessment.
Area of Science:
- Sleep science
- Computational neuroscience
- Artificial intelligence in medicine
Background:
- Current sleep scoring relies on fixed 30-second epochs, which may not accurately represent sleep dynamics.
- This limitation can hinder precise physiological sleep assessment.
Purpose of the Study:
- To develop a deep learning-based sleep-wake classifier with high temporal resolution.
- To utilize temporally continuous manual scoring, bypassing fixed epoch boundaries.
- To improve the physiological consistency of sleep assessment.
Main Methods:
- A U-Net based deep learning model was trained on sleep-wake data.
- Transfer learning was employed, fine-tuning the model with temporally continuous scored data.
- The model was validated on independent datasets using continuous scoring.
Main Results:
- The classifier achieved high concordance (88.96% and 88.23%) with continuous manual scoring.
- Strong correlations were observed between 1-second predictions and manual scoring for total sleep time (r=0.93) and sleep-to-wake transitions (r=0.67).
Conclusions:
- The developed model effectively addresses limitations of traditional 30-second epoch scoring.
- This approach offers a practical foundation for more physiologically consistent sleep-wake assessment.
- Prediction confidence estimates can guide targeted review of potential misclassifications.
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