TY - GEN
T1 - A method to measure ventilation rate during cardiopulmonary resuscitation using the capnogram
AU - Elola, Andoni
AU - Chicote, Beatriz
AU - Aramendi, Elisabete
AU - Alonso, Erik
AU - Irusta, Unai
AU - Daya, Mohamud
AU - Russell, James K.
N1 - Publisher Copyright:
© 2015 CCAL.
PY - 2015/2/16
Y1 - 2015/2/16
N2 - The survival rate in cardiac arrest is associated to the quality of the chest compressions (CCs) and ventilations provided during cardiopulmonary resuscitation (CPR). Hyperventilation remains common whenever ventilation is manual during resuscitation from cardiac arrest. The capnogram is used to monitor respiration and ventilation rates. During CPR chest compressions induce artefacts in the capnogram signal that challenge the detection of ventilations. The evaluation of ventilation detectors during CCs has not been well characterized. In this study an algorithm for ventilation rate monitoring and hyperventilation detection was developed. The processing method consists of detecting transitions in the first difference of the signal, and applying feature based classification to identify every ventilation. The instantaneous rate and hyperventilation minutes were then computed. A set of 20 out-of-hospital episodes, totalling 50864 s (86.6% during CCs) and 6305 ventilations was used to define and evaluate the algorithm. The algorithm had a sensitivity/ positive predictive value of 96.9%/96.2% respectively for the ventilation detection (96.7%/95.8% during ongoing CCs), 98.7%/98.7% for the hyperventilation detection, and a mean error of 0.4 (0.8) min-1 for the instantaneous ventilation rate.
AB - The survival rate in cardiac arrest is associated to the quality of the chest compressions (CCs) and ventilations provided during cardiopulmonary resuscitation (CPR). Hyperventilation remains common whenever ventilation is manual during resuscitation from cardiac arrest. The capnogram is used to monitor respiration and ventilation rates. During CPR chest compressions induce artefacts in the capnogram signal that challenge the detection of ventilations. The evaluation of ventilation detectors during CCs has not been well characterized. In this study an algorithm for ventilation rate monitoring and hyperventilation detection was developed. The processing method consists of detecting transitions in the first difference of the signal, and applying feature based classification to identify every ventilation. The instantaneous rate and hyperventilation minutes were then computed. A set of 20 out-of-hospital episodes, totalling 50864 s (86.6% during CCs) and 6305 ventilations was used to define and evaluate the algorithm. The algorithm had a sensitivity/ positive predictive value of 96.9%/96.2% respectively for the ventilation detection (96.7%/95.8% during ongoing CCs), 98.7%/98.7% for the hyperventilation detection, and a mean error of 0.4 (0.8) min-1 for the instantaneous ventilation rate.
UR - http://www.scopus.com/inward/record.url?scp=84964009624&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84964009624&partnerID=8YFLogxK
U2 - 10.1109/CIC.2015.7411082
DO - 10.1109/CIC.2015.7411082
M3 - Conference contribution
AN - SCOPUS:84964009624
T3 - Computing in Cardiology
SP - 1001
EP - 1004
BT - Computing in Cardiology Conference 2015, CinC 2015
A2 - Murray, Alan
PB - IEEE Computer Society
T2 - 42nd Computing in Cardiology Conference, CinC 2015
Y2 - 6 September 2015 through 9 September 2015
ER -