Validation of an automated algorithm for detecting apneas and hypopneas by acoustic analysis of breath sounds

Background: Sleep-disordered breathing (SDB) is common and is associated with increased risk for cardiovascular disease. However, most patients remain undiagnosed due to lack of access to sleep laboratories. We therefore tested the validity of a single-channel monitoring setup that captures and analyzes breath sounds (BSs) to detect SDB.

Methods: BS were recorded from 50 patients undergoing simultaneous polysomnography (PSG). Using custom-designed automatic software, BS were subjected to a set of pattern recognition rules to identify apneas and hypopneas from which the acoustic apnea-hypopnea index (AHI-a) was calculated. Apneas and hypopneas from PSG were scored blindly by three technicians according to two criteria; one relying solely on the drop of the respiratory signal by >90% for an apnea and by 50% to 90% for a hypopnea (TV50 criteria), and another that also required a desaturation or an arousal for a hypopnea (American Association of Sleep Medicine [AASM] criteria). PSG AHI (AHI-p) was calculated for each technician according to both criteria.

Results: There was no significant difference between AHI-p scores according to TV50 and AASM criteria. AHI-a was strongly correlated with AHI-p according to both TV50 (R=94%) and AASM criteria (R=93%). Bland-Altman plot analysis revealed that 98% and 92% of AHI-a fell within the limits of agreement for AHI-p according to TV50 and AASM criteria, respectively. Based on a diagnostic cutoff of AHI-p≥10 for SDB, overall accuracy of AHI-a reached 88% and negative predictive value reached 100%.

Conclusion: Acoustic analysis of BS is a reliable method for quantifying AHI and diagnosing SDB compared to simultaneous PSG.