Abstract
OBJECTIVE: Common examinations for diagnosing obstructive sleep apnea (OSA) are polysomnography (PSG) and home sleep apnea testing (HSAT). However, both PSG and HSAT require that sensors be attached to a subject, which may disturb their sleep and affect the results. Hence, in this study, we aimed to verify a wireless radar framework combined with deep learning techniques to screen for the risk of OSA in home-based environments. METHODS: This study prospectively collected home-based sleep parameters from 80 participants over 147 nights using both HSAT and a 24-GHz wireless radar framework. The proposed framework, using hybrid models (ie, deep neural decision trees), identified respiratory events by analyzing continuous-wave signals indicative of breathing patterns. Analyses were performed to examine correlations and agreement of the apnea-hypopnea index (AHI) with results obtained through HSAT and the radar-based respiratory disturbance index based on the time in bed from HSAT (bRDI(TIB)). Additionally, Youden's index was used to establish cutoff thresholds for the bRDI(TIB), followed by multiclass classification and outcome comparisons. RESULTS: A strong correlation (ρ = 0.87) and high agreement (93.88% within the 95% confidence interval; 138/147) between the AHI and bRDI(TIB) were identified. The moderate-to-severe OSA model achieved 83.67% accuracy (with a bRDI(TIB) cutoff of 21.19 events/h), and the severe OSA model demonstrated 93.21% accuracy (with a bRDI(TIB) cutoff of 28.14 events/h). The average accuracy of multiclass classification using these thresholds was 78.23%. CONCLUSION: The proposed framework, with its cutoff thresholds, has the potential to be applied in home settings as a surrogate for HSAT, offering acceptable accuracy in screening for OSA without the interference of attached sensors. However, further optimization and verification of the radar-based total sleep time function are necessary for independent application.