background

High-quality sleep is essential for both physical and mental well-being. Insufficient or poor sleep is linked to numerous health issues, including cardiometabolic diseases, mental health disorders, and increased mortality. Snoring, a prevalent condition, can disrupt sleep and is associated with disease states including coronary artery disease and obstructive sleep apnea.

objective

The SleepWatch smartphone application (Bodymatter, Inc., Newport Beach, CA USA) aims to monitor and improve sleep quality and has snore detection capabilities built through a machine-learning process trained on over 60,000 acoustic events. This study evaluates the accuracy of the SleepWatch snore detection algorithm in a simulated real-world setting.

methods

The snore detection algorithm was tested using 36 simulated snoring audio files derived from 18 subjects. Each file simulated a Snoring Index between 30 and 600 snores/hour. Additionally, 9 files with non-snoring sounds were tested to evaluate the algorithm's capacity to avoid false positives. Sensitivity, specificity, and accuracy were calculated for each test, and results were compared using Bland-Altman plots and Spearman correlation to assess the correlation between detected and actual snores.

results

The SleepWatch algorithm showed an average sensitivity of 86.3%, specificity of 99.5%, and accuracy of 95.2% across the snoring tests. The positive predictive value and negative predictive value were 98.9% and 93.8% respectively. The algorithm performed exceptionally well in avoiding false positives, with a specificity of 97.1% for non-snoring files. Inclusive of all snoring and non-snore tests, the aggregated accuracy for all trials in this bench study was 95.6%. Bland-Altman analysis indicated a mean bias of -29.8 snores/hour, and Spearman correlation analysis revealed a strong positive correlation (rs=0.974, P<0.001) between detected and actual snore rates.

conclusions

The SleepWatch snore detection algorithm demonstrates high accuracy and compares favorably with other snore detection applications. Aside from its broader use in sleep monitoring, SleepWatch demonstrates potential as a tool for identifying individuals at risk for sleep disordered breathing, including obstructive sleep apnea, on the basis of Snoring Index.

clinicaltrial