Nocturnal snoring sound analysis in the diagnosis of obstructive sleep apnea in the Chinese Han population

Huajun Xu, Wei Song, Hongliang Yi, Limin Hou, Changheng Zhang, Bin Chen, Yuqin Chen, Shankai Yin
Sleep & Breathing 2015, 19 (2): 599-605

PURPOSE: Loud snoring is one of the principle symptoms of obstructive sleep apnea (OSA). Snoring sound analysis is a potentially cost-effective, reliable alternative for the diagnosis of OSA. However, no investigation has determined the accuracy of snoring signal analysis for the diagnosis of OSA in the Chinese Han population. Therefore, we investigated whether whole-night snoring detection and analysis aids the diagnosis of OSA using a new snore analysis technique.

METHODS: Snoring sounds were recorded using a non-contact microphone and polysomnography (PSG) was performed simultaneously throughout the night. We randomly selected 30 subjects each from four groups based on the severity of OSA. The rhythm and frequency domain of the snoring signal were analyzed based on frequency energy endpoint detection (FEP) and the Earth mover's distance (EMD), for each subject to harvest the EMD-calculated Apnea-Hypopnea Index (AHIEMD). Finally, we compared the AHIEMD with the PSG-monitored AHI (AHIPSG).

RESULTS: The accuracy of the AHIEMD compared with the AHIPSG was 96.7, 86.7, 86.7, and 96.7% in non-, mild, moderate, and severe OSA patients, respectively. AHIEMD was correlated with AHIPSG (r(2) = 0.950, p < 0.001). The area under the receiver operating characteristic curve values for OSA detection was 0.974, 0.957, and 0.997 for AHIEMD thresholds of 5, 15, and 30 events/h, respectively. Bland-Altman analysis revealed 91.7% agreement of AHIEMD with AHIPSG.

CONCLUSIONS: This new method for identifying OSA by analyzing snoring is feasible and reliable in the Han population. The snoring sound-based technique appears to be a promising tool for OSA screening and diagnosis.

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