Abstract
Assessing respiratory and cardiovascular system coupling can provide new insights into disease progression, but requires accurate analysis of each signal. Respiratory waveform data collected during spontaneous breathing are noisy and respiration rates from long term physiological experiments can vary over a wide range across time. There is a need for automatic and robust algorithms to detect breathing peaks in respiration signals for assessment of the coupling between the respiratory and cardiovascular systems. We developed an automatic algorithm to detect breathing peaks from a respiration signal. The algorithm was tested on respiration signals collected during hemorrhage in a conscious ovine model (N=9, total length = 11.0h). The breathing rate varied from 15 to as high as 160 breaths/min for some animals during the hemorrhage protocol. The sensitivity of the algorithm to detect respiration peaks was 93.7% with a precision of 94.5%. The developed algorithm presents a promising approach to detect breathing peaks in respiration signals from spontaneously breathing subjects. The algorithm was able to consistently identify breathing peaks while the breathing rate varied from 15 to 160 breaths/min.
| Original language | English (US) |
|---|---|
| Title of host publication | Computing in Cardiology |
| Publisher | IEEE Computer Society |
| Pages | 297-300 |
| Number of pages | 4 |
| Volume | 42 |
| ISBN (Print) | 9781509006854 |
| DOIs | |
| State | Published - Feb 16 2016 |
| Event | 42nd Computing in Cardiology Conference, CinC 2015 - Nice, France Duration: Sep 6 2015 → Sep 9 2015 |
Other
| Other | 42nd Computing in Cardiology Conference, CinC 2015 |
|---|---|
| Country | France |
| City | Nice |
| Period | 9/6/15 → 9/9/15 |
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ASJC Scopus subject areas
- Cardiology and Cardiovascular Medicine
- Computer Science(all)
Cite this
A robust detection algorithm to identify breathing peaks in respiration signals from spontaneously breathing subjects. / Daluwatte, Chathuri; Scully, Christopher G.; Kramer, George; Strauss, David G.
Computing in Cardiology. Vol. 42 IEEE Computer Society, 2016. p. 297-300 7408645.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A robust detection algorithm to identify breathing peaks in respiration signals from spontaneously breathing subjects
AU - Daluwatte, Chathuri
AU - Scully, Christopher G.
AU - Kramer, George
AU - Strauss, David G.
PY - 2016/2/16
Y1 - 2016/2/16
N2 - Assessing respiratory and cardiovascular system coupling can provide new insights into disease progression, but requires accurate analysis of each signal. Respiratory waveform data collected during spontaneous breathing are noisy and respiration rates from long term physiological experiments can vary over a wide range across time. There is a need for automatic and robust algorithms to detect breathing peaks in respiration signals for assessment of the coupling between the respiratory and cardiovascular systems. We developed an automatic algorithm to detect breathing peaks from a respiration signal. The algorithm was tested on respiration signals collected during hemorrhage in a conscious ovine model (N=9, total length = 11.0h). The breathing rate varied from 15 to as high as 160 breaths/min for some animals during the hemorrhage protocol. The sensitivity of the algorithm to detect respiration peaks was 93.7% with a precision of 94.5%. The developed algorithm presents a promising approach to detect breathing peaks in respiration signals from spontaneously breathing subjects. The algorithm was able to consistently identify breathing peaks while the breathing rate varied from 15 to 160 breaths/min.
AB - Assessing respiratory and cardiovascular system coupling can provide new insights into disease progression, but requires accurate analysis of each signal. Respiratory waveform data collected during spontaneous breathing are noisy and respiration rates from long term physiological experiments can vary over a wide range across time. There is a need for automatic and robust algorithms to detect breathing peaks in respiration signals for assessment of the coupling between the respiratory and cardiovascular systems. We developed an automatic algorithm to detect breathing peaks from a respiration signal. The algorithm was tested on respiration signals collected during hemorrhage in a conscious ovine model (N=9, total length = 11.0h). The breathing rate varied from 15 to as high as 160 breaths/min for some animals during the hemorrhage protocol. The sensitivity of the algorithm to detect respiration peaks was 93.7% with a precision of 94.5%. The developed algorithm presents a promising approach to detect breathing peaks in respiration signals from spontaneously breathing subjects. The algorithm was able to consistently identify breathing peaks while the breathing rate varied from 15 to 160 breaths/min.
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UR - http://www.scopus.com/inward/citedby.url?scp=84964057541&partnerID=8YFLogxK
U2 - 10.1109/CIC.2015.7408645
DO - 10.1109/CIC.2015.7408645
M3 - Conference contribution
AN - SCOPUS:84964057541
SN - 9781509006854
VL - 42
SP - 297
EP - 300
BT - Computing in Cardiology
PB - IEEE Computer Society
ER -