Dataset Information

Enhancing ventilation detection during cardiopulmonary resuscitation by filtering chest compression artifact from the capnography waveform.

ABSTRACT:

Background

During cardiopulmonary resuscitation (CPR), there is a high incidence of capnograms distorted by chest compression artifact. This phenomenon adversely affects the reliability of automated ventilation detection based on the analysis of the capnography waveform. This study explored the feasibility of several filtering techniques for suppressing the artifact to improve the accuracy of ventilation detection.

Materials and methods

We gathered a database of 232 out-of-hospital cardiac arrest defibrillator recordings containing concurrent capnograms, compression depth and transthoracic impedance signals. Capnograms were classified as non-distorted or distorted by chest compression artifact. All chest compression and ventilation instances were also annotated. Three filtering techniques were explored: a fixed-coefficient (FC) filter, an open-loop (OL) adaptive filter, and a closed-loop (CL) adaptive filter. The improvement in ventilation detection was assessed by comparing the performance of a capnogram-based ventilation detection algorithm with original and filtered capnograms.

Results

Sensitivity and positive predictive value of the ventilation algorithm improved from 91.9%/89.5% to 97.7%/96.5% (FC filter), 97.6%/96.7% (OL), and 97.0%/97.1% (CL) for the distorted capnograms (42% of the whole set). The highest improvement was obtained for the artifact named type III, for which performance improved from 77.8%/74.5% to values above 95.5%/94.5%. In addition, errors in the measurement of ventilation rate decreased and accuracy in the detection of over-ventilation increased with filtered capnograms.

Conclusions

Capnogram-based ventilation detection during CPR was enhanced after suppressing the artifact caused by chest compressions. All filtering approaches performed similarly, so the simplicity of fixed-coefficient filters would take advantage for a practical implementation.

SUBMITTER: Gutierrez JJ

PROVIDER: S-EPMC6072040 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

Enhancing ventilation detection during cardiopulmonary resuscitation by filtering chest compression artifact from the capnography waveform.

Gutiérrez Jose Julio JJ Leturiondo Mikel M Ruiz de Gauna Sofía S Ruiz Jesus María JM Leturiondo Luis Alberto LA González-Otero Digna María DM Zive Dana D Russell James Knox JK Daya Mohamud M

PloS one 20180802 8

<h4>Background</h4>During cardiopulmonary resuscitation (CPR), there is a high incidence of capnograms distorted by chest compression artifact. This phenomenon adversely affects the reliability of automated ventilation detection based on the analysis of the capnography waveform. This study explored the feasibility of several filtering techniques for suppressing the artifact to improve the accuracy of ventilation detection.<h4>Materials and methods</h4>We gathered a database of 232 out-of-hospita ...[more]

PMID: 30071008

Similar Datasets

Project description:ObjectivesNew chest compression detection technology allows for the recording and graphical depiction of clinical cardiopulmonary resuscitation (CPR) chest compressions. The authors sought to determine the inter-rater reliability of chest compression pattern classifications by human raters. Agreement with automated chest compression classification was also evaluated by computer analysis.MethodsThis was an analysis of chest compression patterns from cardiac arrest patients enrolled in the ongoing Resuscitation Outcomes Consortium (ROC) Continuous Chest Compressions Trial. Thirty CPR process files from patients in the trial were selected. Using written guidelines, research coordinators from each of eight participating ROC sites classified each chest compression pattern as 30:2 chest compressions, continuous chest compressions (CCC), or indeterminate. A computer algorithm for automated chest compression classification was also developed for each case. Inter-rater agreement between manual classifications was tested using Fleiss's kappa. The criterion standard was defined as the classification assigned by the majority of manual raters. Agreement between the automated classification and the criterion standard manual classifications was also tested.ResultsThe majority of the eight raters classified 12 chest compression patterns as 30:2, 12 as CCC, and six as indeterminate. Inter-rater agreement between manual classifications of chest compression patterns was κ = 0.62 (95% confidence interval [CI] = 0.49 to 0.74). The automated computer algorithm classified chest compression patterns as 30:2 (n = 15), CCC (n = 12), and indeterminate (n = 3). Agreement between automated and criterion standard manual classifications was κ = 0.84 (95% CI = 0.59 to 0.95).ConclusionsIn this study, good inter-rater agreement in the manual classification of CPR chest compression patterns was observed. Automated classification showed strong agreement with human ratings. These observations support the consistency of manual CPR pattern classification as well as the use of automated approaches to chest compression pattern analysis.

Project description:BackgroundThe need for cardiopulmonary resuscitation (CPR) is often unexpected, and the infrequent use of CPR in the delivery room (DR) limits the opportunity to perform rigorous clinical studies to determine the best method for delivering chest compression (CC) to newborn infants. The current neonatal resuscitation guidelines recommend using a coordinated 3:1 compression-to-ventilation (C:V) ratio (CC at a rate of 90/min and ventilations at a rate of 30/min). In comparison, providing CC during a sustained inflation (SI) (CC + SI) significantly improved hemodynamics, minute ventilation, and time to return of spontaneous circulation (ROSC) compared to 3:1 C:V ratio in asphyxiated piglets. Similarly, a small pilot trial in newborn infants showed similar results. Until now no study has examined different CC techniques during neonatal resuscitation in asphyxiated newborn infants in the DR. To date, no trial has been performed to directly compare CC + SI and 3:1 C:V ratio in the DR during CPR of asphyxiated newborn infants.MethodsThis is a large, international, multi-center, prospective, unblinded, cluster randomized controlled trial in asphyxiated newborn infants at birth. All term and preterm infants > 28+ 0 by best obstetrical estimate who require CPR at birth due to bradycardia (< 60/min) or asystole are eligible. The primary outcome of this study is to compare the time to ROSC in infants born > 28+ 0 weeks' gestational age with bradycardia (< 60/min) or asystole immediately after birth who receive either CC + SI or 3:1 C:V ratio as the CPR strategy.DiscussionMorbidity and mortality rates are extremely high for newborns requiring CC. We believe the combination of simultaneous CC and SI during CPR has the potential to significantly improve ROSC and survival. In addition, we believe that CC + SI might improve respiratory and hemodynamic parameters and potentially minimize morbidity and mortality in newborn infants. In addition, this will be the first randomized controlled trial to examine CC in the newborn period.Trial registrationClinicalTrials.gov, NCT02858583 . Registered on 8 August 2016.

Dataset Information

Enhancing ventilation detection during cardiopulmonary resuscitation by filtering chest compression artifact from the capnography waveform.

Background

Materials and methods

Results

Conclusions

Publications

Enhancing ventilation detection during cardiopulmonary resuscitation by filtering chest compression artifact from the capnography waveform.

Similar Datasets

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