Intelligent Snoring Detection Method using the Intervals of Snoring Sound  
Author Byung Mun Lee




Abstract Sleep disorders increase risks such as chronic disease, hypertension, diabetes and cardiovascular disease. It is important to manage snoring during sleep because snoring is the most common respiratory disorder. Some devices and mobile applications for snoring are inadequate to analyze his/her sleep quality because they perform a simple comparison and processing to judge that there is a sleeping disorder if and only if the measured value of the sensor is outside the specified threshold range. In this paper, I proposed a detection snoring duration algorithm to filter the noises by measuring the cyclic difference of the snoring measured by the sound sensor. Moreover, in order to verify the validity of the algorithm, some experiments were performed to find the optimal detection performance by changing the measurement period and the threshold value. Therefore, I succeeded in achieving a good performance such as low error rate over a sampling rate of 10ms and a threshold of 1200.


Keywords Snoring, Intelligent detection method, Healthcare, Sound sensor, IoT
    Article #:  DSIS19-69
Proceedings of ISSAT International Conference on Data Science & Intelligent Systems
August 1-3, 2019 - Las Vegas, NV, U.S.A.