Dependable Algorithm for Visualizing Snoring Duration Through Acoustic Analysis

Abstract

Background: Snoring is a nuisance for the bed partners of people who snore and is also associated with chronic diseases. Estimating the snoring duration from a whole-night-sleep period is challenging. We present a dependable algorithm for visualizing snoring durations through acoustic analysis.Method: Both instruments (Sony digital recorder and smartphone’s SnoreClock app) were placed within 30 cm from the examinee’s head during the sleep period. Subsequently, spectrograms were plotted based on audio files recorded from Sony recorders. We developed an algorithm to validate snoring durations through visualization of typical snoring segments.Results: In total, 37 snoring recordings obtained from six individuals were analyzed. The mean age of the participants was 44.6 ± 9.9 years. A 3-s segment demonstrated the typical dominant frequency bands and amplitude waves of two snores. Every recorded file was tailored to a regular 600-s segment and plotted. Visualization revealed that the typical features of the clustered snores in the amplitude domains were near-isometric spikes (most had an ascending–descending trend). The recorded snores exhibited one or more visibly fixed frequency bands. Intervals were noted between the snoring clusters and were incorporated into the whole-night snoring calculation. The correlative coefficients of snoring rates of digitally recorded files examined by Examiners A and B were higher (0.865, p < 0.001) than those with SnoreClock app (0.757, p < 0.001; 0.787, p < 0.001, respectively).Conclusion: A dependable algorithm with high reproducibility was developed for visualizing snoring durations.