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65. The 4-stage and 5-stage models achieved 68.5% (κ=0.54), and 64.1% (κ=0.51) accuracies, respectively. With the 5-stage model, the total sleep time was underestimated with mean (standard deviation) error of 7.5 (55.2) min. Conclusion The PPG-based deep learning model enabled accurate estimation of sleep time and differentiation between sleep stages with a moderate agreement to manual EEG-based scoring. As PPG is already included in ambulatory polygraphic recordings, applying the PPG-based sleep staging could improve their diagnostic v
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