Hsien-Tsai Wu1, Wen-Yao Pan1, An-Bang Liu2, Mao-Chang Su3, Hong-Ruei Chen1, I-Ting Tsai4, Meng-Chih Lin5, Cheuk-Kwan Sun4. 1. Department of Electrical Engineering, National Dong Hwa University, Hualien, Taiwan. 2. Department of Neurology, Buddhist Tzu Chi General Hospital and Buddhist Tzu Chi University, Hualien, Taiwan. 3. Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan. 4. Department of Emergency Medicine, E-Da Hospital, I-Shou University, Kaohsiung, Taiwan. 5. Sleep Center, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan.
Abstract
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.
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.