The purpose of this research is to collect respiratory sound including snoring among biological sounds generated during sleep, to be used for the development of a personalized sleep management device for the management of routine sleep in everyday life.
The research will include an analysis of sound information generated during sleep, selection of acoustic biomarker, development of a medical algorithm of a new concept based on the acoustic marker which can measure the non-invasive sleep status, development of a smart sleep mask through fusion research, and evaluation of the accuracy and efficacy of these methods using the results of sleep polyvalence test and sleep apnea test as well as a clinical trial and pilot application.
In addition, the goal of this research includes development of a smart sleep mask to a higher level, to be used for the development of various sleep-related health management scenarios using this smart sleep mask and trial application.
- Taehoon Kim, Jeong-Whun Kim, and Kyogu Lee, “Detection of Sleep Disordered Breathing Severity Using Acoustic Biomarker and Machine Learning Techniques”, BioMedical Engineering OnLine, 17(1):16, doi: 10.1186/s12938-018-0448-x, 2018
- Jaepil Kim, Taehoon Kim, Donmoon Lee, Jeong-Whun Kim and Kyogu Lee, “Exploiting temporal and nonstationary features in breathing sound analysis for multiple obstructive sleep apnea severity classification”, BioMedical Engineering Online, Vol. 16, No. 1, 2017
- Jaepil Kim, Taehoon Kim, Jeong-Whun Kim, and Kyogu Lee, “An Investigation of the Snoring Sound Classification Using the Cyclostationary Attributes”, in Proc. of International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, 2016
- Jaepil Kim, Taehoon Kim, Jeong-Whun Kim, and Kyogu Lee, “CYCLOSTATIONARITY-BASED ANALYSIS OF NOCTURNAL RESPIRATION SOUNDS”, in Proc. of International Congress on Sound and Vibration (ICSV), Athens, Greece, 2016