Deep learning-based automated diagnosis of obstructive sleep apnea and sleep stage classification in children using millimeter-wave radar and pulse oximeter.

STUDY

OBJECTIVES

Due to the high cost, complexity, and workload of polysomnography, a radar-based sleep monitoring device, QSA600, has been developed as a more simplified alternative for children. This study evaluates its agreement with polysomnography for obstructive sleep apnea diagnosis and sleep staging.

METHODS

This diagnostic accuracy study included 281 children (1-18 years) who underwent simultaneous polysomnography and QSA600 monitoring at Beijing Children's Hospital from September-November 2023. QSA600 recordings were automatically analyzed using a deep learning model, while polysomnography data were manually scored.

RESULTS

The obstructive apnea-hypopnea index (OAHI) obtained from QSA600 and polysomnography demonstrates a high level of agreement with an intraclass correlation coefficient of 0.945 (95% CI: 0.93-0.96). Bland-Altman analysis indicated that the mean difference of obstructive apnea-hypopnea index between QSA600 and polysomnography was -0.10 events/h (95% CI: -11.15 to 10.96). The deep learning model evaluated through cross-validation showed good sensitivity (81.8%, 84.3%, and 89.7%) and specificity (90.5%, 95.3%, and 97.1%) values for diagnosing children with OAHI >1, OAHI >5, and OAHI >10. The area under the receiver operating characteristic curve was 0.923, 0.955, and 0.988, respectively. For sleep stage classification, the model achieved Kappa coefficients of 0.854, 0.781, and 0.734, with corresponding overall accuracies of 95.0%, 84.8%, and 79.7% for Wake-Sleep classification, Wake-REM-Light-Deep classification, and Wake-REM-N1-N2-N3 classification, respectively.

CONCLUSIONS

QSA600 has demonstrated high agreement with polysomnography in diagnosing obstructive sleep apnea and performing sleep staging in children. The device is portable, low-burden, and suitable for follow-up and long-term pediatric sleep assessment.