Stationary Wavelet Transform for the Extraction of the Impedance Circulation Component during Out-of-hospital Cardiac Arrest

Iraia Isasi, Erik Alonso, Unai Irusta, Elisabete Aramendi, Mohamud R. Daya

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


An automated pulse detector during out-of-hospital cardiac arrest (OHCA) is needed. The thoracic impedance (TI) recorded through defibrillation pads presents an impedance circulation component (ICC), hidden among other components, in the form of small fluctuations correlated with each effective heartbeat. This study pre-sentes a method based on the stationary wavelet transform (SWT) to derive the ICC. A dataset with 456 5-s segments, 175 pulseless electrical activity (PEA) and 281 pulse-generating rhythm (PR), with concurrent ECG and TI signals from 49 OHCA patients was used. The SWT was used to decompose the TI into 7 levels. The ICC was derived from soft denoised d_{6}-d_{7} or d_{7} detail coefficients for segments with heart rate =93 bpm and <93 bpm, respectively. Six features characterizing the amplitude and area of the ICC and its first derivative (dICC) were calculated. Their PEA/PR discrimination power was measured using the area under the curve (AUC). These AUCs were compared with those obtained for the same features derived from the ICC/dICC extracted using an adaptive recursive least-squares (RLS) algorithm. The six features showed a mean (standard deviation) AUC of 0.91 (0.03) while RLS-based features yielded an AUC of 0.85 (0.07). Combining these ICC/dICC features with ECG features in a machine learning classifier might result in a robust pulse detector.

Original languageEnglish (US)
Title of host publication2020 Computing in Cardiology, CinC 2020
PublisherIEEE Computer Society
ISBN (Electronic)9781728173825
StatePublished - Sep 13 2020
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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