Vibration signals of snoring as a simple severity predictor for obstructive sleep apnea

Hsien-Tsai Wu, Wen-Yao Pan, An-Bang Liu, Mao-Chang Su, Hong-Ruei Chen, I-Ting Tsai, Meng-Chih Lin, Cheuk-Kwan Sun
Clinical Respiratory Journal 2016, 10 (4): 440-8

BACKGROUND AND AIM: Polysomnography (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.

METHODS: Totally, 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).

RESULTS: There 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).

CONCLUSION: SBI may serve as a portable tool for screening patients and assessing OSA severity in a non-hospital setting.

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