Open AccessEnd-to-End Sleep Staging Using Nocturnal Sounds from Microphone Chips for Mobile Devices
Author(s) -
Joonki Hong,
Haï Tran,
Jinhwan Jung,
Hyeryung Jang,
Dong Hoon Lee,
In-Young Yoon,
Jung Kyung Hong,
JeongWhun Kim
Publication year - 2022
Publication title -
nature and science of sleep
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.715
H-Index - 34
ISSN - 1179-1608
DOI - 10.2147/nss.s361270
Subject(s) - microphone , polysomnography , computer science , spectrogram , noise (video) , medicine , deep learning , sleep (system call) , mobile device , nocturnal , epoch (astronomy) , artificial intelligence , speech recognition , computer vision , telecommunications , apnea , sound pressure , stars , psychiatry , image (mathematics) , operating system
Nocturnal sounds contain numerous information and are easily obtainable by a non-contact manner. Sleep staging using nocturnal sounds recorded from common mobile devices may allow daily at-home sleep tracking. The objective of this study is to introduce an end-to-end (sound-to-sleep stages) deep learning model for sound-based sleep staging designed to work with audio from microphone chips, which are essential in mobile devices such as modern smartphones.