Detection of sleep disordered breathing severity using acoustic biomarker and machine learning techniques.

Journal: Biomedical engineering online

Published Date: Feb 1, 2018

Abstract

PURPOSE: Breathing sounds during sleep are altered and characterized by various acoustic specificities in patients with sleep disordered breathing (SDB). This study aimed to identify acoustic biomarkers indicative of the severity of SDB by analyzing the breathing sounds collected from a large number of subjects during entire overnight sleep.

Authors

Taehoon Kim

Music and Audio Research Group, Graduate School of Convergence Science and Technology, Seoul National University, 1 Gwanak-ro, Seoul, 08826, Republic of Korea.
Jeong-Whun Kim

Department of Otorhinolaryngology, Seoul National University Bundang Hospital, 82, Gumi-ro, Bundang-gu, Seongnam, Republic of Korea.
Kyogu Lee

Music and Audio Research Group, Graduate School of Convergence Science and Technology, Seoul National University, Gwanak-Gu, Seoul, The Republic of Korea.

Keywords

Acoustics Adolescent Adult Aged Biomarkers Body Mass Index Child Female Humans Machine Learning Male Middle Aged Polysomnography Sleep Sleep Apnea Syndromes Snoring Young Adult

External Resources

View on PubMed Access via DOI PubMed (29391025)

Detection of sleep disordered breathing severity using acoustic biomarker and machine learning techniques.

Abstract

Authors

Keywords

External Resources

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