Classification of respiratory effort and disordered breathing during sleep from audio and pulse oximetry signals

Brian R. Snider, Alexander Kain

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages794-798
Number of pages5
Volume2016-May
ISBN (Electronic)9781479999880
DOIs
StatePublished - May 18 2016
Event41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China
Duration: Mar 20 2016Mar 25 2016

Other

Other41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
CountryChina
CityShanghai
Period3/20/163/25/16

Fingerprint

Medical problems
Screening
Acoustics
Acoustic waves
Oxygen
Sleep

Keywords

  • breathing
  • polysomnography
  • sleep apnea

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

Cite this

Snider, B. R., & Kain, A. (2016). Classification of respiratory effort and disordered breathing during sleep from audio and pulse oximetry signals. In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings (Vol. 2016-May, pp. 794-798). [7471784] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2016.7471784

Classification of respiratory effort and disordered breathing during sleep from audio and pulse oximetry signals. / Snider, Brian R.; Kain, Alexander.

2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings. Vol. 2016-May Institute of Electrical and Electronics Engineers Inc., 2016. p. 794-798 7471784.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Snider, BR & Kain, A 2016, Classification of respiratory effort and disordered breathing during sleep from audio and pulse oximetry signals. in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings. vol. 2016-May, 7471784, Institute of Electrical and Electronics Engineers Inc., pp. 794-798, 41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016, Shanghai, China, 3/20/16. https://doi.org/10.1109/ICASSP.2016.7471784
Snider BR, Kain A. Classification of respiratory effort and disordered breathing during sleep from audio and pulse oximetry signals. In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings. Vol. 2016-May. Institute of Electrical and Electronics Engineers Inc. 2016. p. 794-798. 7471784 https://doi.org/10.1109/ICASSP.2016.7471784
Snider, Brian R. ; Kain, Alexander. / Classification of respiratory effort and disordered breathing during sleep from audio and pulse oximetry signals. 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings. Vol. 2016-May Institute of Electrical and Electronics Engineers Inc., 2016. pp. 794-798
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