Finger Photoplethysmography to Monitor Chest Compression Rate during Out-of-Hospital Cardiac Arrest

Andoni Elola, Jon Urteaga, Elisabete Aramendi, Unai Irusta, Erik Alonso, Mohamud Daya, Pamela Owens, Ahamed Idris

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

2 Scopus citations


Cardiac arrest survival rate is strongly associated with high quality cardiopulmonary resuscitation (CPR), which includes chest compression (CC) rates above 100 min-1. Currently, defibrillator monitors use external hardware such as CPR assist pads to monitor CC rate and give feedback to the rescuer. The photoplethysmogram (PPG) provides information about the level of oxygen saturation in blood and can be easily recorded by a pulse oximeter in the fingertip. The aim of this study was to analyze the feasibility of using the finger PPG to monitor the presence and rate of CCs in out-of-hospital cardiac arrest (OHCA). The dataset used in the study consisted of 112 segments from 46 OHCA patients, with a total duration of 256 min and 27667 CCs. The method is based on the power spectral density analysis of 10 s segments of the PPG. CC presence was determined through thresholding, and CC rate was computed applying a maximum slope criterion. The dataset was divided patient-wise intro training (60%) and testing (40%) sets. For the test set the algorithm presented a sensitivity and a positive predictive value of 85.2% and 98.1% respectively for CC detection, a CC rate error of 2.8 (6.8)min-1 and 3.4% of the values with an error above 10%.

Original languageEnglish (US)
Title of host publicationComputing in Cardiology Conference, CinC 2018
PublisherIEEE Computer Society
ISBN (Electronic)9781728109589
StatePublished - Sep 2018
Event45th Computing in Cardiology Conference, CinC 2018 - Maastricht, Netherlands
Duration: Sep 23 2018Sep 26 2018

Publication series

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


Conference45th Computing in Cardiology Conference, CinC 2018

ASJC Scopus subject areas

  • General Computer Science
  • Cardiology and Cardiovascular Medicine


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