TY - JOUR
T1 - Closed-loop adaptive filtering for supressing chest compression oscillations in the capnogram during cardiopulmonary resuscitation
AU - Leturiondo, Mikel
AU - Gutierrez, J. J.
AU - De Gauna, Sofía Ruiz
AU - Plaza, Sandra
AU - Veintemillas, José F.
AU - Daya, Mohamud
N1 - Publisher Copyright:
© 2017 IEEE Computer Society. All rights reserved.
PY - 2017
Y1 - 2017
N2 - Capnography is widely used by the advanced-life-support during cardiopulmonary resuscitation (CPR). Continuous analysis of the capnogram allows guidance of adequate ventilation rate, currently 10 breaths/min for intubated patients. We used 60 out-of-hospital cardiac arrest episodes containing both clean and CC corrupted capnograms. Chest compressions (CC) induce high-frequency oscillations in the capnography waveform impeding reliable detection of ventilations. Thus, a clean capnogram is essential for a better ventilation detection performance. To clean the capnogram, an adaptive noise cancellation notch filter was designed using a Least Mean Square algorithm to minimize filtering error. A fixed-coefficient low-pass filter was optimized for comparison. For the whole test set, global Se/PPV improved from 93.0/92.2% to 97.6/96.2% after adaptive filtering and to 97.7/94.8% after fixed-coefficient filtering. For the clean subset, Se/PPV maintained stable and for the corrupted subset, Se/PPV improved from 84.8/84.0% to 95.2/92.7% and 95.4/90.3%, respectively. Filtering allowed reliable automated detection of ventilations in the capnogram even in the presence of CC oscillations during CPR. Nevertheless, further evaluation of these techniques in large datasets is warranted.
AB - Capnography is widely used by the advanced-life-support during cardiopulmonary resuscitation (CPR). Continuous analysis of the capnogram allows guidance of adequate ventilation rate, currently 10 breaths/min for intubated patients. We used 60 out-of-hospital cardiac arrest episodes containing both clean and CC corrupted capnograms. Chest compressions (CC) induce high-frequency oscillations in the capnography waveform impeding reliable detection of ventilations. Thus, a clean capnogram is essential for a better ventilation detection performance. To clean the capnogram, an adaptive noise cancellation notch filter was designed using a Least Mean Square algorithm to minimize filtering error. A fixed-coefficient low-pass filter was optimized for comparison. For the whole test set, global Se/PPV improved from 93.0/92.2% to 97.6/96.2% after adaptive filtering and to 97.7/94.8% after fixed-coefficient filtering. For the clean subset, Se/PPV maintained stable and for the corrupted subset, Se/PPV improved from 84.8/84.0% to 95.2/92.7% and 95.4/90.3%, respectively. Filtering allowed reliable automated detection of ventilations in the capnogram even in the presence of CC oscillations during CPR. Nevertheless, further evaluation of these techniques in large datasets is warranted.
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U2 - 10.22489/CinC.2017.008-079
DO - 10.22489/CinC.2017.008-079
M3 - Conference article
AN - SCOPUS:85045093584
SN - 2325-8861
VL - 44
SP - 1
EP - 4
JO - Computing in Cardiology
JF - Computing in Cardiology
T2 - 44th Computing in Cardiology Conference, CinC 2017
Y2 - 24 September 2017 through 27 September 2017
ER -