Validation of a new snoring detection device based on a hysteresis extraction algorithm

Publication date: Available online 1 February 2017
Source:Auris Nasus Larynx
Author(s): Hirotaka Hara, Masakazu Tsutsumi, Syunsuke Tarumoto, Toshikazu Shiga, Hiroshi Yamashita
ObjectiveThis 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.MethodsThirty 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.ResultsStudy 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%.ConclusionsThis 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.



http://ift.tt/2kuiKtr