An Impedance-based Algorithm to Detect Ventilations during Cardiopulmonary Resuscitation

X. Jaureguibeitia, U. Irusta, E. Aramendi, H. E. Wang, A. H. Idris

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Cardiopulmonary resuscitation (CPR) is a core therapy to treat out-of-hospital cardiac arrest (OHCA). Thoracic impedance (TI) can be used to assess ventilations during CPR, but the signal is also affected by chest compression (CC) artifacts. This study presents a method for TI-based ventilation detection during concurrent manual CCs. Data from 152 OHCA patients were analyzed. A total of 423 TI segments of at least 60 s during ongoing CCs were extracted. True ventilations were annotated using the capnogram. The final dataset comprised 1210 min of TI recordings and 9665 ground truth ventilations. A three-stage detection algorithm was developed. First, the TI signal was filtered to obtain ventilation waveforms, including a least mean squares filter to remove artifacts due to CCs. Potential ventilations were then identified with a heuristic detector and characterized by a set of 16 features. These were finally fed to a random forest classifier to discriminate between true ventilations and false positives. Patients were split into 100 distinct training (70%) and test (30%) partitions. The median (interquartile range) sensitivity, PPV and F-score were 83.9 (70.2-91.2) %, 86.1 (75.0-93.3) % and 84.3 (72.1-91.4) %. Our method would allow feedback on ventilation rates as well as surrogate measures of insufflated air volume during CPR.

Original languageEnglish (US)
Title of host publication2020 Computing in Cardiology, CinC 2020
PublisherIEEE Computer Society
ISBN (Electronic)9781728173825
StatePublished - Sep 13 2020
Externally publishedYes
Event2020 Computing in Cardiology, CinC 2020 - Rimini, Italy
Duration: Sep 13 2020Sep 16 2020

Publication series

NameComputing in Cardiology
ISSN (Print)2325-8861
ISSN (Electronic)2325-887X


Conference2020 Computing in Cardiology, CinC 2020

ASJC Scopus subject areas

  • Computer Science(all)
  • Cardiology and Cardiovascular Medicine


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