Hirotaka Hara1, Masakazu Tsutsumi2, Syunsuke Tarumoto3, Toshikazu Shiga2, Hiroshi Yamashita3. 1. Department of Otolaryngology, Yamaguchi University Graduate School of Medicine, Ube, Yamaguchi 755-8505, Japan. Electronic address: harahiro@yamaguchi-u.ac.jp. 2. Omron Healthcare Co., Ltd., Kyoto 617-0002, Japan. 3. Department of Otolaryngology, Yamaguchi University Graduate School of Medicine, Ube, Yamaguchi 755-8505, Japan.
Abstract
OBJECTIVE: This paper aims to introduce and validate our newly developed snoring detection device to automatically identify the incidence and amplitude of snores using the hysteresis extraction method. METHODS: Thirty patients (16 males and 14 females) with a history of snoring were included in this study. Each patient underwent a conventional polysomnography (PSG). Natural overnight snoring was recorded from each subject using our original snore detection device and an integrated circuit (IC) recorder while the patient slept during PSG. A new algorithm based on hysteresis extraction was used to detect snores and qualify the level of each event at 30-s intervals (one epoch). The automated and subjective assessment concordance was evaluated by comparing a total of 27,295 epochs, and sensitivity, specificity, and accuracy were calculated. RESULTS: Study population analysis revealed a mean rate of snore time against the total sleep time of 14.1±7.9%. Further, validation of the automatic snore detection revealed the following: sensitivity, 71.2%; specificity, 93.1%; positive predictive value, 77.7%; negative predictive value, 94.6%; and accuracy, 90.7%. CONCLUSIONS: This study revealed the efficacy of our newly developed snoring detection device and indicated that it may serve as a useful method in further snoring analysis via objective medical assessment. However, the sample size of 30 subjects was relatively small; therefore, further research is needed to evaluate this device.
OBJECTIVE: This paper aims to introduce and validate our newly developed snoring detection device to automatically identify the incidence and amplitude of snores using the hysteresis extraction method. METHODS: Thirty patients (16 males and 14 females) with a history of snoring were included in this study. Each patient underwent a conventional polysomnography (PSG). Natural overnight snoring was recorded from each subject using our original snore detection device and an integrated circuit (IC) recorder while the patient slept during PSG. A new algorithm based on hysteresis extraction was used to detect snores and qualify the level of each event at 30-s intervals (one epoch). The automated and subjective assessment concordance was evaluated by comparing a total of 27,295 epochs, and sensitivity, specificity, and accuracy were calculated. RESULTS: Study population analysis revealed a mean rate of snore time against the total sleep time of 14.1±7.9%. Further, validation of the automatic snore detection revealed the following: sensitivity, 71.2%; specificity, 93.1%; positive predictive value, 77.7%; negative predictive value, 94.6%; and accuracy, 90.7%. CONCLUSIONS: This study revealed the efficacy of our newly developed snoring detection device and indicated that it may serve as a useful method in further snoring analysis via objective medical assessment. However, the sample size of 30 subjects was relatively small; therefore, further research is needed to evaluate this device.