A Review of the Performance of Artifact Filtering Algorithms for Cardiopulmonary Resuscitation

Gong, Yushun; Chen, Bihua; Li, Yongqin

doi:10.1260/2040-2295.4.2.185

Cited by 31 publications

(23 citation statements)

References 56 publications

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“…Previously, the AUC of most waveform measures during active chest compressions was unknown, although reduced prediction was expected since chest compressions produce artifact that obscure the ECG across a wide range of frequencies. 12,46 Indeed, we found that even after optimization for use during chest compressions, the predictive performance of individual waveform measures declined during CPR. Thus the use of these individual measures to guide resuscitation would likely require repeated interruptions in chest compressions to accurately gauge the heart’s physiological status, undermining the clinical benefits of CPR.…”

Section: Discussionmentioning

confidence: 84%

Ventricular Fibrillation Waveform Analysis During Chest Compressions to Predict Survival From Cardiac Arrest

Coult

Blackwood

Sherman

et al. 2019

Circ: Arrhythmia and Electrophysiology

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Background: Quantitative measures of the ventricular fibrillation (VF) electrocardiogram (ECG) waveform can assess myocardial physiology and predict cardiac arrest outcomes, making these measures a candidate to help guide resuscitation. Chest compressions are typically paused for waveform measure calculation, as compressions cause ECG artifact. However, such pauses contradict resuscitation guideline recommendations to minimize CPR interruptions. We evaluated a comprehensive group of VF measures with and without ongoing compressions to determine their performance under both conditions for predicting functionally-intact survival, the study’s primary outcome. Methods: Five-second VF ECG segments were collected with and without chest compressions prior to 2755 defibrillation shocks from 1151 out-of-hospital cardiac arrest patients. Twenty-four individual measures and three combination measures were implemented. Measures were optimized to predict functionally-intact survival (Cerebral Performance Category score ≤ 2) using 460 training cases, and their performance evaluated using 691 independent test cases. Results: Measures predicted functionally-intact survival on test data with an area under the receiver operating characteristic curve (AUC) ranging from 0.56-0.75 (median=0.73) without chest compressions and from 0.53-0.75 (median=0.69) with compressions (p<0.001 for difference). Of all measures evaluated, the support vector machine model ranked highest both without chest compressions (AUC=0.75, 95% CI 0.73-0.78) and with compressions (AUC=0.75, 95% CI 0.72-0.78) (p=0.75 for difference). Conclusions: VF waveform measures predict functionally-intact survival when calculated during chest compressions, but prognostic performance is generally reduced compared to compression-free analysis. However, support vector machine models exhibited similar performance with and without compressions while also achieving the highest AUC. Such machine learning models may therefore offer means to guide resuscitation during uninterrupted CPR.

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Section: Discussionmentioning

confidence: 84%

Ventricular Fibrillation Waveform Analysis During Chest Compressions to Predict Survival From Cardiac Arrest

Coult

Blackwood

Sherman

et al. 2019

Circ: Arrhythmia and Electrophysiology

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“…Efficacy of the fixed-coefficient filter may be affected by the variability of chest compression and ventilation rates during CPR [17,18,30,42]. In the literature, filters adjusted in time, according to the varying characteristics of the artifact, have been extensively used to suppress oscillations in the electrocardiogram induced by chest compressions [43][44][45][46]. In this study, we designed two adaptive filtering configurations, an open-loop and a closed-loop adaptive filter [47].…”

Section: Adaptive Filteringmentioning

confidence: 99%

Waveform Capnography for Monitoring Ventilation during Cardiopulmonary Resuscitation: The Problem of Chest Compression Artifact

Leturiondo¹,

Gauna²,

Gutiérrez³

et al. 2019

Cardiac Diseases and Interventions in 21st Century

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Sudden cardiac arrest (SCA) is the sudden cessation of the heart's effective pumping function, confirmed by the absence of pulse and breathing. Without appropriate treatment, it leads to sudden cardiac death, considered responsible for half of the global cardiac disease deaths. Cardiopulmonary resuscitation (CPR) is a key intervention during SCA. Current resuscitation guidelines emphasize the use of waveform capnography during CPR in order to enhance CPR quality and improve patient outcomes. Capnography represents the concentration of the partial pressure of carbon dioxide (CO 2) in respiratory gases and reflects ventilation and perfusion of the patient. Waveform capnography should be used for confirming the correct placement of the tracheal tube and monitoring ventilation. Other potential uses of capnography in resuscitation involve monitoring CPR quality, early identification of restoration of spontaneous circulation (ROSC), and determination of patient prognosis. An important role of waveform capnography is ventilation rate monitoring to prevent overventilation. However, some studies have reported the appearance of high-frequency oscillations synchronized with chest compressions superimposed on the capnogram. This chapter explores the incidence of chest compression artifact in out-of-hospital capnograms, assesses its negative influence in the automated detection of ventilations, and proposes several methods to enhance ventilation detection and capnography waveform.

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“…Filtering approaches have been widely proposed to remove chest compression artifact from the electrocardiogram during CPR [5]. In a recent study, we proposed a few filtering techniques to remove those oscillations from the capnogram [6].…”

Section: Introductionmentioning

confidence: 99%

A Method to Suppress Chest Compression Artifact Enhancing Capnography-Based Ventilation Guidance During Cardiopulmonary Resuscitation

Leturiondo

Gutierrez

Gauna

et al. 2018

2018 Computing in Cardiology Conference (CinC)

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Capnography-based ventilation rate guidance is valuable and widely used by advanced life support during cardiopulmonary resuscitation (CPR). However, there is a high incidence of induced chest compression (CC) oscillations that decreases the reliability of automated ventilation detection. We used 30 out-of-hospital cardiac arrest episodes containing the capnogram and transthoracic impedance signals. The algorithm detects the presence of distorted ventilations in the capnogram. It calculates the artifact envelope during the alveolar plateau and removes the artifact during capnogram baseline, thus obtaining a non-distorted waveform. The goodness of the method was assessed by comparing the performance of a ventilation detection algorithm before and after artifact suppression. From a total of 6387 annotated ventilations, 34% of them were classified as distorted. Global sensitivity and positive predictive value (Se/PPV, %) improved from 77.9/74.0 to 97.0/95.8. Median value of the unsigned error (%) of the estimated ventilation rate decreased from 19.6 to 4.5 and the accuracy for detection of over-ventilation increased with cleaned capnograms. Capnogram-based ventilation guidance during CPR was enhanced after CC artifact suppression. Our method preserved the tracing of CO2 concentration caused by ventilations, allowing other clinical uses of the capnography during resuscitation.

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A Review of the Performance of Artifact Filtering Algorithms for Cardiopulmonary Resuscitation

Cited by 31 publications

References 56 publications

Ventricular Fibrillation Waveform Analysis During Chest Compressions to Predict Survival From Cardiac Arrest

Ventricular Fibrillation Waveform Analysis During Chest Compressions to Predict Survival From Cardiac Arrest

Waveform Capnography for Monitoring Ventilation during Cardiopulmonary Resuscitation: The Problem of Chest Compression Artifact

A Method to Suppress Chest Compression Artifact Enhancing Capnography-Based Ventilation Guidance During Cardiopulmonary Resuscitation

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