• Hsien-Tsai Wu, Wen-Yao Pan, An-Bang Liu, Mao-Chang Su, Hong-Ruei Chen, I-Ting Tsai, Meng-Chih Lin, and Cheuk-Kwan Sun.
• Department of Electrical Engineering, National Dong Hwa University, Hualien, Taiwan.
• Clin Respir J. 2016 Jul 1; 10 (4): 440-8.

Background And AimPolysomnography (PSG), which involves simultaneous monitoring of various physiological monitors, is the current comprehensive tool for diagnosing obstructive sleep apnea (OSA). We aimed at validating vibrating signals of snoring as a single physiological parameter for screening and evaluating severity of OSA.MethodsTotally, 111 subjects from the sleep center of a tertiary referral center were categorized into four groups according to the apnea hypopnea index (AHI) obtained from PSG: simple snoring group (5 > AHI, healthy subjects, n = 11), mild OSA group (5 ≤ AHI < 15, n = 11), moderate OSA group (15 ≤ AHI < 30, n = 30) and severe OSA group (AHI ≥ 30, n = 59). Anthropometric parameters and sleep efficiency of all subjects were compared. Frequencies of amplitude changes of vibrating signals on anterior neck during sleep were analyzed to acquire a snoring burst index (SBI) using a novel algorithm. Data were compared with AHI and index of arterial oxygen saturation (Δ Index).ResultsThere were no significant differences in age and sleep efficiency among all groups. Bland-Altman analysis showed better agreement between SBI and AHI (r = 0.906, P < 0.001) than Δ Index and AHI (r = 0.859, P < 0.001). Additionally, receiver operating characteristic (ROC) showed substantially stronger sensitivity and specificity of SBI in distinguishing between patients with moderate and severe OSA compared with Δ Index (sensitivity: 81.4% vs 66.4%; specificity: 96.7% vs 86.7%, for SBI and Δ Index, respectively).ConclusionSBI may serve as a portable tool for screening patients and assessing OSA severity in a non-hospital setting.© 2014 John Wiley & Sons Ltd.

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