Monitoring chest compression rate in automated external defibrillators using the autocorrelation of the transthoracic impedance

PLoS One. 2020 Sep 30;15(9):e0239950. doi: 10.1371/journal.pone.0239950. eCollection 2020.

Abstract

Aim: High-quality chest compressions is challenging for bystanders and first responders to out-of-hospital cardiac arrest (OHCA). Long compression pauses and compression rates higher than recommended are common and detrimental to survival. Our aim was to design a simple and low computational cost algorithm for feedback on compression rate using the transthoracic impedance (TI) acquired by automated external defibrillators (AEDs).

Methods: ECG and TI signals from AED recordings of 242 OHCA patients treated by basic life support (BLS) ambulances were retrospectively analyzed. Beginning and end of chest compression series and each individual compression were annotated. The algorithm computed a biased estimate of the autocorrelation of the TI signal in consecutive non-overlapping 2-s analysis windows to detect the presence of chest compressions and estimate compression rate.

Results: A total of 237 episodes were included in the study, with a median (IQR) duration of 10 (6-16) min. The algorithm performed with a global sensitivity in the detection of chest compressions of 98.7%, positive predictive value of 98.7%, specificity of 97.1%, and negative predictive value of 97.1% (validation subset including 207 episodes). The unsigned error in the estimation of compression rate was 1.7 (1.3-2.9) compressions per minute.

Conclusion: Our algorithm is accurate and robust for real-time guidance on chest compression rate using AEDs. The algorithm is simple and easy to implement with minimal software modifications. Deployment of AEDs with this capability could potentially contribute to enhancing the quality of chest compressions in the first minutes from collapse.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Cardiography, Impedance
  • Cardiopulmonary Resuscitation / methods*
  • Databases, Factual
  • Defibrillators*
  • Electrocardiography
  • Humans
  • Monitoring, Physiologic / methods
  • Out-of-Hospital Cardiac Arrest / diagnosis
  • Out-of-Hospital Cardiac Arrest / therapy*
  • Pressure
  • Retrospective Studies
  • Sensitivity and Specificity

Grants and funding

The Basque Government provided support in the form of a grant for research groups (IT1087-16) for authors Sofía Ruiz de Gauna, Jesus María Ruiz, and Jose Julio Gutiérrez. The Spanish Ministry of Economy, Industry and Competitiveness provided support in the form of a grant for research projects (RTI2018-094396-B-I00) for authors Sofía Ruiz de Gauna, Jesus María Ruiz, and Jose Julio Gutiérrez; and in the form of the program Torres Quevedo (PTQ-16-08201) for author Digna María González-Otero. The University of the Basque Country (UPV/EHU) provided support in the form of a grant for collaboration between research groups and companies (US18/30) for authors Sofía Ruiz de Gauna, Jesus María Ruiz, and Jose Julio Gutiérrez. Bexen Cardio, a Spanish medical device manufacturer, provided support in the form of a salary for author Digna Mara Gonzalez-Otero. None of the above funding organizations had any additional role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific role of each author is articulated in the Author Contributions section. Authors Daniel Alonso, Carlos Corcuera, and Juan Francisco Urtusagasti received no funding for this work.