Open-loop adaptive filtering for suppressing chest compression oscillations in the capnogram during cardiopulmonary resuscitation

Mikel Leturiondo, Jesús Ruiz, J. J. Gutiérrez, Luis A. Leturiondo, James K. Russell, Mohamud Ramzan Daya

Research output: Contribution to journalConference article

Abstract

Capnography is often used for the guidance on ventilation rate during cardiopulmonary resuscitation (CPR). However, capnogram waveform frequently presents oscillations induced by chest compressions (CC), affecting the reliability of ventilation detection. The aim of the work was to evaluate the performance of an open-loop adaptive filter in the cancellation of CC oscillations in the capnogram during CPR. For that purpose, we analyzed 60 episodes from an out-of-hospital (OOH) cardiac arrest registry maintained by TVF&R agency (USA). In 50% of the episodes the capnogram was corrupted by CC oscillations. The goodness of the filtering scheme was assessed by comparing the sensitivity (Se) and the positive predictive value (PPV) of an automated ventilation detector before and after filtering. A fixed-coefficient low-pass filter was also designed for comparison. The results showed that both filters reported a good performance although the adaptive scheme presented a slightly higher PPV (+1.2 points globally). The simpler fixed-coefficient scheme avoids the reference signal, but requires validation with larger datasets to ensure stability.

Original languageEnglish (US)
Pages (from-to)1-4
Number of pages4
JournalComputing in Cardiology
Volume44
DOIs
StatePublished - Jan 1 2017
Event44th Computing in Cardiology Conference, CinC 2017 - Rennes, France
Duration: Sep 24 2017Sep 27 2017

ASJC Scopus subject areas

  • Computer Science(all)
  • Cardiology and Cardiovascular Medicine

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