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

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. (C) 2017 Elsevier B.V. All rights reserved.