Fig. 4: Performance evaluation of sleep parameters estimation.
From: Advancing sleep health equity through deep learning on large-scale nocturnal respiratory signals
a–g Comparison of sleep parameters (TST, SE, SOL, WASO, and proportions of Light sleep, Deep sleep, and REM sleep) between the actual and predicted values across the internal dataset (n = 3030 nights), ClinHuaiAn dataset (n = 424 nights), and ClinRadar dataset (n = 221 nights). Each violin plot shows the distribution of the differences (prediction error) between the true and predicted values. The contour of each violin plot indicates the kernel density of these differences, while the horizontal line inside represents the median, the box bounds indicate the 25th and 75th percentiles, and the whiskers extend to the minimum and maximum values within 1.5 times the interquartile range (IQR). The numerical values above each plot represent the Pearson correlation coefficients between the actual and predicted parameters, and the significance of the correlations is assessed using a two-tailed Pearson correlation test with exact p-values. All n values refer to independent nights of sleep recordings. Source data are provided as a Source Data file. h–m Kaplan-Meier plots of the true and predicted average continuous sleep times in the internal dataset (h, i), the ClinHuaiAn dataset (j, k), and the ClinRadar dataset (l, m). The vertical axis indicates the survival probability (proportion of subjects with an average continuous sleep time greater than the duration shown on the horizontal axis). Different colored curves represent subjects in distinct sleep apnea-hypopnea severity categories (No OSA, Mild, Moderate, Severe). The shaded areas surrounding each curve denote the 95% confidence intervals of the survival probability estimates.
