Automatic sleep-stage scoring based on photoplethysmographic signals

Sleep-stage scoring is important for sleep-quality evaluation and the diagnosis of related diseases. In this study, an automatic sleep-stage scoring method using photoplethysmographic (PPG) signals was proposed.

To construct the classification model, we extracted 14 time-domain features, 17 frequency-domain features, and 20 pulse rate variability (PRV) features along with four SpO₂ features from PPG signals. An artificial neural network classifier was used to integrate the results of ten binary support vector machine classifiers and realise sleep-stage classification. Leave-one-subject-out validation was applied to evaluate our proposed model.

Thirty-one subjects were enrolled in the study, in which 21 subjects were with high sleep quality (sleep efficiencies ⩾85%). Our model achieved accuracies of 57% (κ = 0.39), 62% (κ = 0.41), and 78% (κ = 0.54) for the classification of five sleep stages (wake, N1, N2, N3, and rapid eye movement (REM) sleeps), four sleep stages (wake, light, deep, and REM sleeps) and three sleep stages (wake, non-rapid eye movement (NREM), and REM sleeps), respectively. For the remaining ten subjects with poor sleep quality, the results came to 55% (κ = 0.39), 62% (κ = 0.43), and 75% (κ = 0.52).

The satisfactory performance of our proposed model reveals the potential of PPG signals for sleep-stage scoring, which may contribute to automatic sleep monitoring in the home environment.