@inproceedings{0d4aa7f8ba2a4646bf77d5175ef9c027,
title = "Classification of respiratory effort and disordered breathing during sleep from audio and pulse oximetry signals",
abstract = "Sleep-disordered breathing (SDB) is a highly prevalent condition associated with many adverse health problems. As the current means of diagnosis (polysomnography) is obtrusive and ill-suited for mass screening of the population, we explore a minimal-contact, automatic approach that uses acoustics-based methods in conjunction with pulse oximetry. We present a two-stage method for automatically classifying breathing sounds produced during sleep to track respiratory effort and predicting disordered breathing events using respiratory effort durations and oxygen desaturations. We compare our method for tracking respiratory effort and predicting disordered breathing with human expert event scoring. Our subject-independent method tracks respiratory effort with 87% accuracy and predicts disordered breathing events with 40-52% accuracy.",
keywords = "breathing, polysomnography, sleep apnea",
author = "Snider, {Brian R.} and Alexander Kain",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 ; Conference date: 20-03-2016 Through 25-03-2016",
year = "2016",
month = may,
day = "18",
doi = "10.1109/ICASSP.2016.7471784",
language = "English (US)",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "794--798",
booktitle = "2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings",
}