Monitoring chest compression quality during cardiopulmonary resuscitation: Proof-ofconcept of a single accelerometer-based feedback algorithm

Digna María Gonzáleza-Otero, Jesus María Ruiz, Sofía Ruiz De Gauna, Jose Julio Gutiérrez, Mohamud Ramzan Daya, James Knox Russell, Izaskun Azcarate, Mikel Leturiondo

Research output: Contribution to journalArticle

Abstract

Background The use of real-Time feedback systems to guide rescuers during cardiopulmonary resuscitation (CPR) significantly contributes to improve adherence to published resuscitation guidelines. Recently, we designed a novel method for computing depth and rate of chest compressions relying solely on the spectral analysis of chest acceleration. That method was extensively tested in a simulated manikin scenario. The purpose of this study is to report the results of this method as tested in human out-of-hospital cardiac arrest (OHCA) cases. Materials and methods The algorithm was evaluated retrospectively with seventy five OHCA episodes recorded by monitor-defibrillators equipped with a CPR feedback device. The acceleration signal and the compression signal computed by the CPR feedback device were stored in each episode. The algorithm was continuously applied to the acceleration signals. The depth and rate values estimated every 2-s from the acceleration data were compared to the reference values obtained from the compression signal. The performance of the algorithm was assesed in terms of the sensitivity and positive predictive value (PPV) for detecting compressions and in terms of its accuracy through the analysis of measurement error. Results The algorithm reported a global sensitivity and PPV of 99.98% and 99.79%, respectively. The median (P75) unsigned error in depth and rate was 0.9 (1.7) mm and 1.0 (1.7) cpm, respectively. In 95% of the analyzed 2-s windows the error was below 3.5 mm and 3.1 cpm, respectively. Conclusions The CPR feedback algorithm proved to be reliable and accurate when tested retrospectively with human OHCA episodes. A new CPR feedback device based on this algorithm could be helpful in the resuscitation field.

Original languageEnglish (US)
Article numbere0192810
JournalPLoS One
Volume13
Issue number2
DOIs
StatePublished - Feb 1 2018

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cardiopulmonary resuscitation
Resuscitation
Cardiopulmonary Resuscitation
chest
Accelerometers
Thorax
Out-of-Hospital Cardiac Arrest
Feedback
cardiac arrest
Monitoring
monitoring
Equipment and Supplies
Manikins
Defibrillators
Computer Systems
methodology
spectral analysis
normal values
Measurement errors
Reference Values

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

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Gonzáleza-Otero, D. M., Ruiz, J. M., De Gauna, S. R., Gutiérrez, J. J., Daya, M. R., Russell, J. K., ... Leturiondo, M. (2018). Monitoring chest compression quality during cardiopulmonary resuscitation: Proof-ofconcept of a single accelerometer-based feedback algorithm. PLoS One, 13(2), [e0192810]. https://doi.org/10.1371/journal.pone.0192810

Monitoring chest compression quality during cardiopulmonary resuscitation : Proof-ofconcept of a single accelerometer-based feedback algorithm. / Gonzáleza-Otero, Digna María; Ruiz, Jesus María; De Gauna, Sofía Ruiz; Gutiérrez, Jose Julio; Daya, Mohamud Ramzan; Russell, James Knox; Azcarate, Izaskun; Leturiondo, Mikel.

In: PLoS One, Vol. 13, No. 2, e0192810, 01.02.2018.

Research output: Contribution to journalArticle

Gonzáleza-Otero, DM, Ruiz, JM, De Gauna, SR, Gutiérrez, JJ, Daya, MR, Russell, JK, Azcarate, I & Leturiondo, M 2018, 'Monitoring chest compression quality during cardiopulmonary resuscitation: Proof-ofconcept of a single accelerometer-based feedback algorithm', PLoS One, vol. 13, no. 2, e0192810. https://doi.org/10.1371/journal.pone.0192810
Gonzáleza-Otero, Digna María ; Ruiz, Jesus María ; De Gauna, Sofía Ruiz ; Gutiérrez, Jose Julio ; Daya, Mohamud Ramzan ; Russell, James Knox ; Azcarate, Izaskun ; Leturiondo, Mikel. / Monitoring chest compression quality during cardiopulmonary resuscitation : Proof-ofconcept of a single accelerometer-based feedback algorithm. In: PLoS One. 2018 ; Vol. 13, No. 2.
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abstract = "Background The use of real-Time feedback systems to guide rescuers during cardiopulmonary resuscitation (CPR) significantly contributes to improve adherence to published resuscitation guidelines. Recently, we designed a novel method for computing depth and rate of chest compressions relying solely on the spectral analysis of chest acceleration. That method was extensively tested in a simulated manikin scenario. The purpose of this study is to report the results of this method as tested in human out-of-hospital cardiac arrest (OHCA) cases. Materials and methods The algorithm was evaluated retrospectively with seventy five OHCA episodes recorded by monitor-defibrillators equipped with a CPR feedback device. The acceleration signal and the compression signal computed by the CPR feedback device were stored in each episode. The algorithm was continuously applied to the acceleration signals. The depth and rate values estimated every 2-s from the acceleration data were compared to the reference values obtained from the compression signal. The performance of the algorithm was assesed in terms of the sensitivity and positive predictive value (PPV) for detecting compressions and in terms of its accuracy through the analysis of measurement error. Results The algorithm reported a global sensitivity and PPV of 99.98{\%} and 99.79{\%}, respectively. The median (P75) unsigned error in depth and rate was 0.9 (1.7) mm and 1.0 (1.7) cpm, respectively. In 95{\%} of the analyzed 2-s windows the error was below 3.5 mm and 3.1 cpm, respectively. Conclusions The CPR feedback algorithm proved to be reliable and accurate when tested retrospectively with human OHCA episodes. A new CPR feedback device based on this algorithm could be helpful in the resuscitation field.",
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AU - Ruiz, Jesus María

AU - De Gauna, Sofía Ruiz

AU - Gutiérrez, Jose Julio

AU - Daya, Mohamud Ramzan

AU - Russell, James Knox

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N2 - Background The use of real-Time feedback systems to guide rescuers during cardiopulmonary resuscitation (CPR) significantly contributes to improve adherence to published resuscitation guidelines. Recently, we designed a novel method for computing depth and rate of chest compressions relying solely on the spectral analysis of chest acceleration. That method was extensively tested in a simulated manikin scenario. The purpose of this study is to report the results of this method as tested in human out-of-hospital cardiac arrest (OHCA) cases. Materials and methods The algorithm was evaluated retrospectively with seventy five OHCA episodes recorded by monitor-defibrillators equipped with a CPR feedback device. The acceleration signal and the compression signal computed by the CPR feedback device were stored in each episode. The algorithm was continuously applied to the acceleration signals. The depth and rate values estimated every 2-s from the acceleration data were compared to the reference values obtained from the compression signal. The performance of the algorithm was assesed in terms of the sensitivity and positive predictive value (PPV) for detecting compressions and in terms of its accuracy through the analysis of measurement error. Results The algorithm reported a global sensitivity and PPV of 99.98% and 99.79%, respectively. The median (P75) unsigned error in depth and rate was 0.9 (1.7) mm and 1.0 (1.7) cpm, respectively. In 95% of the analyzed 2-s windows the error was below 3.5 mm and 3.1 cpm, respectively. Conclusions The CPR feedback algorithm proved to be reliable and accurate when tested retrospectively with human OHCA episodes. A new CPR feedback device based on this algorithm could be helpful in the resuscitation field.

AB - Background The use of real-Time feedback systems to guide rescuers during cardiopulmonary resuscitation (CPR) significantly contributes to improve adherence to published resuscitation guidelines. Recently, we designed a novel method for computing depth and rate of chest compressions relying solely on the spectral analysis of chest acceleration. That method was extensively tested in a simulated manikin scenario. The purpose of this study is to report the results of this method as tested in human out-of-hospital cardiac arrest (OHCA) cases. Materials and methods The algorithm was evaluated retrospectively with seventy five OHCA episodes recorded by monitor-defibrillators equipped with a CPR feedback device. The acceleration signal and the compression signal computed by the CPR feedback device were stored in each episode. The algorithm was continuously applied to the acceleration signals. The depth and rate values estimated every 2-s from the acceleration data were compared to the reference values obtained from the compression signal. The performance of the algorithm was assesed in terms of the sensitivity and positive predictive value (PPV) for detecting compressions and in terms of its accuracy through the analysis of measurement error. Results The algorithm reported a global sensitivity and PPV of 99.98% and 99.79%, respectively. The median (P75) unsigned error in depth and rate was 0.9 (1.7) mm and 1.0 (1.7) cpm, respectively. In 95% of the analyzed 2-s windows the error was below 3.5 mm and 3.1 cpm, respectively. Conclusions The CPR feedback algorithm proved to be reliable and accurate when tested retrospectively with human OHCA episodes. A new CPR feedback device based on this algorithm could be helpful in the resuscitation field.

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