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. 2025 Jun 18:12:1617530.
doi: 10.3389/fmed.2025.1617530. eCollection 2025.

Validation of the Somnolyzer 24×7 automatic scoring system in children with suspected obstructive sleep apnea

Ignacio Boira  1 , Violeta Esteban  1 , José Norberto Sancho-Chust  1 , Esther Pastor  1 , Paula Fernández-Martínez  1 , Anastasiya Torba  1 , Eusebi Chiner  1
Affiliations

Validation of the Somnolyzer 24×7 automatic scoring system in children with suspected obstructive sleep apnea

Ignacio Boira et al. Front Med (Lausanne). .

Abstract

Introduction: Manual scoring of polysomnography data is a laborious and complex process. Automatic scoring by current computer algorithms shows high agreement with manual scoring. The primary objective of this study was to measure the overall validity of the Somnolyzer 24×7 automatic polysomnography scoring system in children.

Materials and methods: We conducted a single-center, prospective, observational study in children undergoing diagnostic polysomnography for suspected obstructive sleep apnea (OSA) from December 2023 to December 2024. We included children aged three to 15 years with suspected obstructive sleep apnea (OSA). Each polysomnogram was scored manually by three experts and automatically by the Somnolyzer 24×7 system.

Results: Our analysis included 75 children (60% girls), of whom 9% did not have OSA, 20% had mild OSA, 31% moderate OSA, and 40% severe OSA. There was a high level of agreement between manual and automatic scoring of the respiratory disturbance index (RDI). The mean correlation (Pearson correlation coefficient) of RDI scored by the three experts was 0.93 (95% confidence interval [CI] 0.92-0.95), similar to the correlation between manual and automatic scoring (0.92, 95% CI 0.90-0.94). The correlation between the different manual scorings and between manual and automatic scoring was maintained in the different sleep stages (N1: 0.93 vs. 0.90, N2: 0.76 vs. 0.73, N3: 0.72 vs. 0.76, REM: 0.86 vs. 0.82).

Conclusion: The Somnolyzer 24×7 automatic scoring system shows strong correlation with manual scoring in respiratory events and sleep architecture. Our results suggest this system could be used for polysomnography scoring in children.

Keywords: Somnolyzer; artificial intelligence; children; obstructive sleep apnea; polysomnography; sleep disorders.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Correlation matrix (left panel) and Bland–Altman (right panel) plots for the respiratory disturbance index (RDI). Pearson correlation coefficients and the average bias (95% confidence interval) are in the left and right panels, respectively.
Figure 2
Figure 2
Correlation matrix (left panel) and Bland–Altman (right panel) plots for the sleep stage N1 (%). Pearson correlation coefficients and the average bias (95% confidence interval) are in the left and right panels, respectively.
Figure 3
Figure 3
Correlation matrix (left panel) and Bland–Altman (right panel) plots for the sleep stage N2 (%). Pearson correlation coefficients and the average bias (95% confidence interval) are in the left and right panels, respectively.
Figure 4
Figure 4
Correlation matrix (left panel) and Bland–Altman (right panel) plots for the sleep stage N3 (%). Pearson correlation coefficients and the average bias (95% confidence interval) are in the left and right panels, respectively.
Figure 5
Figure 5
Correlation matrix (left panel) and Bland–Altman (right panel) plots for the sleep stage rapid eye movement (REM). Pearson correlation coefficients and the average bias (95% confidence interval) are in the left and right panels, respectively.
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