Open AccessMonitoring chest compression rate in automated external defibrillators using the autocorrelation of the transthoracic impedance
Author(s) -
Sofía Ruiz de Gauna,
J. Ruiz,
J.J. Gutiérrez,
Digna M. González-Otero,
Daniel Alonso,
Carlos Corcuera,
Juan Francisco Urtusagasti
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0239950
Subject(s) - medicine , compression (physics) , data compression ratio , cardiopulmonary resuscitation , autocorrelation , data compression , cardiology , algorithm , computer science , surgery , statistics , resuscitation , image compression , artificial intelligence , mathematics , composite material , image processing , materials science , image (mathematics)
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.