Feasibility of the capnogram to monitor ventilation rate during cardiopulmonary resuscitation.
The rates of chest compressions (CCs) and ventilations are both important metrics to monitor the quality of cardiopulmonary resuscitation (CPR). Capnography permits monitoring ventilation, but the CCs provided during CPR corrupt the capnogram and compromise the accuracy of automatic ventilation detectors. The aim of this study was to evaluate the feasibility of an automatic algorithm based on the capnogram to detect ventilations and provide feedback on ventilation rate during CPR, specifically addressing intervals where CCs are delivered.



The dataset used to develop and test the algorithm contained in-hospital and out-of-hospital cardiac arrest episodes. The method relies on adaptive thresholding to detect ventilations in the first derivative of the capnogram. The performance of the detector was reported in terms of sensitivity (SE) and Positive Predictive Value (PPV). The overall performance was reported in terms of the rate error and errors in the hyperventilation alarms. Results were given separately for the intervals with CCs.



A total of 83 episodes were considered, resulting in 4880min and 46,740 ventilations (8741 during CCs). The method showed an overall SE/PPV above 99% and 97% respectively, even in intervals with CCs. The error for the ventilation rate was below 1.8min-1in any group, and >99% of the ventilation alarms were correctly detected.



A method to provide accurate feedback on ventilation rate using only the capnogram is proposed. Its accuracy was proven even in intervals where canpography signal was severely corrupted by CCs. This algorithm could be integrated into monitor/defibrillators to provide reliable feedback on ventilation rate during CPR.
Communications Engineering Department, University of the Basque Country UPV/EHU, Alameda Urquijo S/N, 48013 Bilbao, Spain. Electronic address: elisabete.aramendi@ehu.es.
Elisabete
E
Aramendi
Communications Engineering Department, University of the Basque Country UPV/EHU, Alameda Urquijo S/N, 48013 Bilbao, Spain.
Andoni
A
Elola
Department of Applied Mathematics, University of the Basque Country UPV/EHU, Rafael Moreno "Pitxitxi", 3, 48013 Bilbao, Spain.
Erik
E
Alonso
Communications Engineering Department, University of the Basque Country UPV/EHU, Alameda Urquijo S/N, 48013 Bilbao, Spain.
Unai
U
Irusta
Department of Emergency Medicine, Oregon Health & Science University, 97239-3098 Portland, OR, United States.
Mohamud
M
Daya
Department of Emergency Medicine, Oregon Health & Science University, 97239-3098 Portland, OR, United States.
James K
JK
Russell
Department of Emergency Medicine, Medical University of Vienna, 1090 Wien, Austria.
Pia
P
Hubner
Department of Emergency Medicine, Medical University of Vienna, 1090 Wien, Austria.
Fritz
F
Sterz
D000465:Algorithms; D019296:Capnography / Q000379:methods*; D016887:Cardiopulmonary Resuscitation / Q000009:adverse effects / Q000379:methods; D061827:Dimensional Measurement Accuracy; D005240:Feasibility Studies; D006323:Heart Arrest / Q000175:diagnosis / Q000503:physiopathology / Q000628:therapy; D006801:Humans; D006985:Hyperventilation / Q000209:etiology / Q000517:prevention & control; D008991:Monitoring, Physiologic; D011237:Predictive Value of Tests; D012123:Pulmonary Ventilation / Q000502:physiology*; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted
Capnography; Cardiopulmonary resuscitation; Hyperventilation; Ventilation monitoring