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 language | English (US) |
|---|---|
| Title of host publication | 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 794-798 |
| Number of pages | 5 |
| Volume | 2016-May |
| ISBN (Electronic) | 9781479999880 |
| DOIs | |
| State | Published - May 18 2016 |
| Event | 41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China Duration: Mar 20 2016 → Mar 25 2016 |
Other
| Other | 41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 |
|---|---|
| Country | China |
| City | Shanghai |
| Period | 3/20/16 → 3/25/16 |
Fingerprint
Keywords
- breathing
- polysomnography
- sleep apnea
ASJC Scopus subject areas
- Signal Processing
- Software
- Electrical and Electronic Engineering
Cite this
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 proceeding › Conference contribution
}
TY - GEN
T1 - Classification of respiratory effort and disordered breathing during sleep from audio and pulse oximetry signals
AU - Snider, Brian R.
AU - Kain, Alexander
PY - 2016/5/18
Y1 - 2016/5/18
N2 - 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.
AB - 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.
KW - breathing
KW - polysomnography
KW - sleep apnea
UR - http://www.scopus.com/inward/record.url?scp=84973312243&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84973312243&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2016.7471784
DO - 10.1109/ICASSP.2016.7471784
M3 - Conference contribution
AN - SCOPUS:84973312243
VL - 2016-May
SP - 794
EP - 798
BT - 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
ER -