Literature DB >> 17875356

A method to remove CPR artefacts from human ECG using only the recorded ECG.

Sofia Ruiz de Gauna1, Jesus Ruiz, Unai Irusta, Elisabete Aramendi, Trygve Eftestøl, Jo Kramer-Johansen.   

Abstract

AIM: To show the possibility of using cardiopulmonary resuscitation (CPR) artefact suppression methods that do not need additional reference signals to model CPR artefacts.
MATERIALS AND METHODS: A CPR suppression method based on a Kalman filter was designed. The artefact was modelled using the fundamental frequency of the compressions, estimated from the spectral analysis of the ECG. Artificial mixtures of human shockable rhythms and CPR artefacts were used to design the algorithm that was then tested on samples obtained from real out-of-hospital cardiac arrest episodes.
RESULTS: The shock/no-shock decision of an automated external defibrillator (AED) was evaluated before and after CPR suppression for 131 shockable and 347 non-shockable samples. The sensitivity improved from 56% (95% CI, 47-64%) to 90% (95% CI, 84-94%). However, the specificity decreased from 91% (95% CI, 87-93%) to 80% (95% CI, 76-84%).
CONCLUSIONS: CPR artefacts can be suppressed using methods based on the analysis of the ECG alone. The hardware of current AEDs does not need to be replaced, although better artefact suppression methods exist for modified AEDs with additional reference channels.

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Mesh:

Year:  2007        PMID: 17875356     DOI: 10.1016/j.resuscitation.2007.08.002

Source DB:  PubMed          Journal:  Resuscitation        ISSN: 0300-9572            Impact factor:   5.262


  8 in total

1.  Removal of cardiopulmonary resuscitation artifacts with an enhanced adaptive filtering method: an experimental trial.

Authors:  Yushun Gong; Tao Yu; Bihua Chen; Mi He; Yongqin Li
Journal:  Biomed Res Int       Date:  2014-03-27       Impact factor: 3.411

2.  Estimation of the variations in mechanical impedance between the actuator and the chest, and the power delivered to the chest during cardiopulmonary resuscitation using machine-embedded sensors.

Authors:  Seong Wook Choi; Do Yeon Lee; Kyoung Won Nam
Journal:  Biomed Eng Online       Date:  2018-06-19       Impact factor: 2.819

3.  Deep Neural Network Approach for Continuous ECG-Based Automated External Defibrillator Shock Advisory System During Cardiopulmonary Resuscitation.

Authors:  Shirin Hajeb-M; Alicia Cascella; Matt Valentine; K H Chon
Journal:  J Am Heart Assoc       Date:  2021-03-05       Impact factor: 5.501

4.  Estimating the amplitude spectrum area of ventricular fibrillation during cardiopulmonary resuscitation using only ECG waveform.

Authors:  Feng Zuo; Youde Ding; Chenxi Dai; Liang Wei; Yushun Gong; Juan Wang; Yiming Shen; Yongqin Li
Journal:  Ann Transl Med       Date:  2021-04

5.  Automated Condition-Based Suppression of the CPR Artifact in ECG Data to Make a Reliable Shock Decision for AEDs during CPR.

Authors:  Shirin Hajeb-Mohammadalipour; Alicia Cascella; Matt Valentine; Ki H Chon
Journal:  Sensors (Basel)       Date:  2021-12-08       Impact factor: 3.576

6.  Reduction of CPR artifacts in the ventricular fibrillation ECG by coherent line removal.

Authors:  Anton Amann; Andreas Klotz; Thomas Niederklapfer; Alexander Kupferthaler; Tobias Werther; Marcus Granegger; Wolfgang Lederer; Michael Baubin; Werner Lingnau
Journal:  Biomed Eng Online       Date:  2010-01-06       Impact factor: 2.819

Review 7.  Rhythm analysis during cardiopulmonary resuscitation: past, present, and future.

Authors:  Sofia Ruiz de Gauna; Unai Irusta; Jesus Ruiz; Unai Ayala; Elisabete Aramendi; Trygve Eftestøl
Journal:  Biomed Res Int       Date:  2014-01-09       Impact factor: 3.411

8.  A reliable method for rhythm analysis during cardiopulmonary resuscitation.

Authors:  U Ayala; U Irusta; J Ruiz; T Eftestøl; J Kramer-Johansen; F Alonso-Atienza; E Alonso; D González-Otero
Journal:  Biomed Res Int       Date:  2014-05-07       Impact factor: 3.411

  8 in total

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