@inproceedings{5d03ca12732f445aa1a8b95ead7fe837,
title = "Automatic classification of breathing sounds during sleep",
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 non-contact, automatic approach that uses acoustics-based methods. We present a method for automatically classifying breathing sounds produced during sleep. We compare the performance of several acoustic feature representations for detecting diagnostically-relevant sleep breathing events to predict overall SDB severity. Our subject-independent method tracks rest in the breathing cycle with 84-87% accuracy, and predicts SDB severity at a level similar to polysomnography.",
keywords = "breathing, polysomnography, sleep apnea",
author = "Snider, {Brian R.} and Alexander Kain",
year = "2013",
month = oct,
day = "18",
doi = "10.1109/ICASSP.2013.6637738",
language = "English (US)",
isbn = "9781479903566",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
pages = "699--703",
booktitle = "2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings",
note = "2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 ; Conference date: 26-05-2013 Through 31-05-2013",
}