Empirical mode decomposition for chest compression and ventilation detection in cardiac arrest

Erik Alonso, Elisabete Aramendi, Digna González-Otero, Unai Ayala, Mohamud Daya, James K. Russell

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations


The thoracic impedance (TI) signal, which reflects fluctuations due to CCs and ventilations, has been suggested as a surrogate to compute CC-rate and ventilation-rate during cardiopulmonary resuscitation. This study developed a method based on empirical mode decomposition (EMD) to compute CC-rate and ventilation-rate using exclusively the TI. Twenty out-of-hospital cardiac arrest episodes containing the TI, compression depth (gold standard for CC-rate), and capnography (gold standard for ventilation-rate) signals were used. The EMD decomposed the TI signal into intrinsic mode functions (IMFs). IMFs were combined based on their median instantaneous frequency to reconstruct separately the CC-signal and the ventilation-signal. Independent CC and ventilation detectors were used based on fixed thresholds for durations and dynamic thresholds for the amplitudes of the fluctuations. Sensitivity and positive predictive value (PPV) for each detector were 99.35%/98.75% and 93.21%/82.40%. CC-rate and ventilation-rate were computed based on instants of CCs and ventilations respectively. When comparing detected rates with rates obtained from the gold standards, the mean (SD) errors were 0.57(0.55) min-1 and 1.10 (1.19) min -1 for CC-rate and ventilation-rate respectively. We concluded that CC-rate and ventilation-rate can be accurately estimated applying EMD to the TI.

Original languageEnglish (US)
Article number7042968
Pages (from-to)17-20
Number of pages4
JournalComputing in Cardiology
Issue numberJanuary
StatePublished - 2014
Event41st Computing in Cardiology Conference, CinC 2014 - Cambridge, United States
Duration: Sep 7 2014Sep 10 2014

ASJC Scopus subject areas

  • Computer Science(all)
  • Cardiology and Cardiovascular Medicine


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