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Enhancing ventilation detection during cardiopulmonary resuscitation by filtering chest compression artifact from the capnography waveform

BACKGROUND: During cardiopulmonary resuscitation (CPR), there is a high incidence of capnograms distorted by chest compression artifact. This phenomenon adversely affects the reliability of automated ventilation detection based on the analysis of the capnography waveform. This study explored the fea...

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Autores principales: Gutiérrez, Jose Julio, Leturiondo, Mikel, Ruiz de Gauna, Sofía, Ruiz, Jesus María, Leturiondo, Luis Alberto, González-Otero, Digna María, Zive, Dana, Russell, James Knox, Daya, Mohamud
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6072040/
https://www.ncbi.nlm.nih.gov/pubmed/30071008
http://dx.doi.org/10.1371/journal.pone.0201565
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author Gutiérrez, Jose Julio
Leturiondo, Mikel
Ruiz de Gauna, Sofía
Ruiz, Jesus María
Leturiondo, Luis Alberto
González-Otero, Digna María
Zive, Dana
Russell, James Knox
Daya, Mohamud
author_facet Gutiérrez, Jose Julio
Leturiondo, Mikel
Ruiz de Gauna, Sofía
Ruiz, Jesus María
Leturiondo, Luis Alberto
González-Otero, Digna María
Zive, Dana
Russell, James Knox
Daya, Mohamud
author_sort Gutiérrez, Jose Julio
collection PubMed
description BACKGROUND: During cardiopulmonary resuscitation (CPR), there is a high incidence of capnograms distorted by chest compression artifact. This phenomenon adversely affects the reliability of automated ventilation detection based on the analysis of the capnography waveform. This study explored the feasibility of several filtering techniques for suppressing the artifact to improve the accuracy of ventilation detection. MATERIALS AND METHODS: We gathered a database of 232 out-of-hospital cardiac arrest defibrillator recordings containing concurrent capnograms, compression depth and transthoracic impedance signals. Capnograms were classified as non-distorted or distorted by chest compression artifact. All chest compression and ventilation instances were also annotated. Three filtering techniques were explored: a fixed-coefficient (FC) filter, an open-loop (OL) adaptive filter, and a closed-loop (CL) adaptive filter. The improvement in ventilation detection was assessed by comparing the performance of a capnogram-based ventilation detection algorithm with original and filtered capnograms. RESULTS: Sensitivity and positive predictive value of the ventilation algorithm improved from 91.9%/89.5% to 97.7%/96.5% (FC filter), 97.6%/96.7% (OL), and 97.0%/97.1% (CL) for the distorted capnograms (42% of the whole set). The highest improvement was obtained for the artifact named type III, for which performance improved from 77.8%/74.5% to values above 95.5%/94.5%. In addition, errors in the measurement of ventilation rate decreased and accuracy in the detection of over-ventilation increased with filtered capnograms. CONCLUSIONS: Capnogram-based ventilation detection during CPR was enhanced after suppressing the artifact caused by chest compressions. All filtering approaches performed similarly, so the simplicity of fixed-coefficient filters would take advantage for a practical implementation.
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spelling pubmed-60720402018-08-16 Enhancing ventilation detection during cardiopulmonary resuscitation by filtering chest compression artifact from the capnography waveform Gutiérrez, Jose Julio Leturiondo, Mikel Ruiz de Gauna, Sofía Ruiz, Jesus María Leturiondo, Luis Alberto González-Otero, Digna María Zive, Dana Russell, James Knox Daya, Mohamud PLoS One Research Article BACKGROUND: During cardiopulmonary resuscitation (CPR), there is a high incidence of capnograms distorted by chest compression artifact. This phenomenon adversely affects the reliability of automated ventilation detection based on the analysis of the capnography waveform. This study explored the feasibility of several filtering techniques for suppressing the artifact to improve the accuracy of ventilation detection. MATERIALS AND METHODS: We gathered a database of 232 out-of-hospital cardiac arrest defibrillator recordings containing concurrent capnograms, compression depth and transthoracic impedance signals. Capnograms were classified as non-distorted or distorted by chest compression artifact. All chest compression and ventilation instances were also annotated. Three filtering techniques were explored: a fixed-coefficient (FC) filter, an open-loop (OL) adaptive filter, and a closed-loop (CL) adaptive filter. The improvement in ventilation detection was assessed by comparing the performance of a capnogram-based ventilation detection algorithm with original and filtered capnograms. RESULTS: Sensitivity and positive predictive value of the ventilation algorithm improved from 91.9%/89.5% to 97.7%/96.5% (FC filter), 97.6%/96.7% (OL), and 97.0%/97.1% (CL) for the distorted capnograms (42% of the whole set). The highest improvement was obtained for the artifact named type III, for which performance improved from 77.8%/74.5% to values above 95.5%/94.5%. In addition, errors in the measurement of ventilation rate decreased and accuracy in the detection of over-ventilation increased with filtered capnograms. CONCLUSIONS: Capnogram-based ventilation detection during CPR was enhanced after suppressing the artifact caused by chest compressions. All filtering approaches performed similarly, so the simplicity of fixed-coefficient filters would take advantage for a practical implementation. Public Library of Science 2018-08-02 /pmc/articles/PMC6072040/ /pubmed/30071008 http://dx.doi.org/10.1371/journal.pone.0201565 Text en © 2018 Gutiérrez et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Gutiérrez, Jose Julio
Leturiondo, Mikel
Ruiz de Gauna, Sofía
Ruiz, Jesus María
Leturiondo, Luis Alberto
González-Otero, Digna María
Zive, Dana
Russell, James Knox
Daya, Mohamud
Enhancing ventilation detection during cardiopulmonary resuscitation by filtering chest compression artifact from the capnography waveform
title Enhancing ventilation detection during cardiopulmonary resuscitation by filtering chest compression artifact from the capnography waveform
title_full Enhancing ventilation detection during cardiopulmonary resuscitation by filtering chest compression artifact from the capnography waveform
title_fullStr Enhancing ventilation detection during cardiopulmonary resuscitation by filtering chest compression artifact from the capnography waveform
title_full_unstemmed Enhancing ventilation detection during cardiopulmonary resuscitation by filtering chest compression artifact from the capnography waveform
title_short Enhancing ventilation detection during cardiopulmonary resuscitation by filtering chest compression artifact from the capnography waveform
title_sort enhancing ventilation detection during cardiopulmonary resuscitation by filtering chest compression artifact from the capnography waveform
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6072040/
https://www.ncbi.nlm.nih.gov/pubmed/30071008
http://dx.doi.org/10.1371/journal.pone.0201565
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