Characterisation of upper airway obstructions using wide-band snoring sounds

Abstract

Background: Obstructive sleep apnea (OSA) is associated with partial (hypopnea) and full (apnea) upper airway obstructions. The standard method for identifying these obstructions and leading to OSA diagnosis is overnight polysomnography (PSG). One of the important outcomes of PSG is the apnea-hypopnea index (AHI), which represents the average number of obstructions per hour, computed over the total duration of sleep. AHI is a single number summary of the dynamics of the upper airway during sleep. PSG is complicated, requires more than 20 body-contact sensors and is not available for population screening. There is a need for simple and automated technologies that can characterise the upper airways in more detail. In this paper, we address this need by proposing a method to identify individual apnea and hypopnea events via the analysis of breathing and snoring sounds collected through a microphone, which requires no physical contact with a subject. We also explore snoring and breathing sounds over a wide frequency band, exceeding the capacity of human hearing. Hypothesis: Our primary hypothesis is that snoring sounds carry sufficient information to characterise partial and full collapses of the upper airway in OSA. We also hypothesise that snoring sounds have a frequency spectrum extending beyond the human hearing range, and the spectrum's high-frequency components can provide critical information on airway collapses. Method: We recorded streams of respiratory sounds using a bedside microphone (4 Hz–100 kHz) from patients undergoing PSG studies in a sleep laboratory. Audio streams were divided into non-overlapping segments of 5 s durations. Segments containing partial or full snores were manually extracted for further investigation. Altogether we extracted 19,715 such segments from 18 subjects. We then obtained the power spectrum of each of the segments and estimated spectral energies in 16 frequency bands covering the range: 4 Hz–35 kHz. Significantly, when the upper airway narrows during or in the vicinity of apnea/hypopnea events, spectral energy tends to shift to higher frequencies. In this research, we developed indices which capture these energy shifts and function as surrogate measures of airway patency. We then calibrated proposed sound based indices using PSG derived apnea and hypopnea events. Our indices allowed us to individually pick apnea and hypopnea events and estimate a sound based AHI which we named the frequency spectrum index. Results: Our results indicated that snoring sounds extend to frequencies beyond the upper limit of the nominal hearing range, i.e. 20 kHz (p < 0.0001). At the patient level, the frequency spectrum index shows a correlation of r = 0.80 with the PSG based hypopnea index, with the highest correlation in the frequency band of 5–15 kHz. Similarly, the frequency spectrum index showed a correlation of r = 0.82 with the PSG based obstructive apnea index, where, interestingly, the highest correlation occurs in the 25–30 kHz band, which is outside the human range of hearing band. Conclusion: The snoring sound based frequency spectrum index has the potential to characterise full and partial closures of the upper airway. Snoring sounds are spread over a wide spectrum and critical information on the full closure of the upper airway is predominantly carried in frequencies beyond human hearing capacity.