Impedance-Based Ventilation Detection During Cardiopulmonary Resuscitation

It has been suggested to develop automated external defibrillators with the ability to monitor cardiopulmonary resuscitation (CPR) performance online and give corrective feedback in order to improve the resuscitation quality. Thoracic impedance changes are closely correlated to lung volume changes a...

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Published in:IEEE transactions on biomedical engineering Vol. 54; no. 12; pp. 2237 - 2245
Main Authors: Risdal, Martin, Aase, Sven Ole, Stavland, Mette, Eftestol, Trygve
Format: Journal Article
Language:English
Published: United States IEEE 01-12-2007
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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Abstract It has been suggested to develop automated external defibrillators with the ability to monitor cardiopulmonary resuscitation (CPR) performance online and give corrective feedback in order to improve the resuscitation quality. Thoracic impedance changes are closely correlated to lung volume changes and can be used to monitor the ventilatory activity. We developed a pattern-recognition-based detection system that uses thoracic impedance to accurately detect ventilation during ongoing CPR. The detection system was developed and evaluated on recordings of real-world resuscitation efforts of cardiac arrest patients where ventilations were manually annotated by human experts. The annotated ventilations were detected with an overall positive predictive value of 95.5% for a sensitivity of 90.4%. During chest compressions, the detection system achieved a mean positive predictive value of 94.8% for a sensitivity of 88.7%. The results suggest that accurate ventilation detection during CPR based on the proposed approach is feasible, and that the performance is not significantly degraded in the presence of chest compressions.
AbstractList It has been suggested to develop automated external defibrillators with the ability to monitor cardiopulmonary resuscitation (CPR) performance online and give corrective feedback in order to improve the resuscitation quality. Thoracic impedance changes are closely correlated to lung volume changes and can be used to monitor the ventilatory activity. We developed a pattern-recognition-based detection system that uses thoracic impedance to accurately detect ventilation during ongoing CPR. The detection system was developed and evaluated on recordings of real-world resuscitation efforts of cardiac arrest patients where ventilations were manually annotated by human experts. The annotated ventilations were detected with an overall positive predictive value of 95.5% for a sensitivity of 90.4%. During chest compressions, the detection system achieved a mean positive predictive value of 94.8% for a sensitivity of 88.7%. The results suggest that accurate ventilation detection during CPR based on the proposed approach is feasible, and that the performance is not significantly degraded in the presence of chest compressions.
It has been suggested to develop automated external defibrillators with the ability to monitor cardiopulmonary resuscitation (CPR) performance online and give corrective feedback in order to improve the resuscitation quality. Thoracic impedance changes are closely correlated to lung volume changes and can be used to monitor the ventilatory activity. We developed a pattern-recognition-based detection system that uses thoracic impedance to accurately detect ventilation during ongoing CPR. The detection system was developed and evaluated on recordings of real-world resuscitation efforts of cardiac arrest patients where ventilations were manually annotated by human experts. The annotated ventilations were detected with an overall positive predictive value of 95.5% for a sensitivity of 90.4%. During chest compressions, the detection system achieved a mean positive predictive value of 94.8% for a sensitivity of 88.7%. The results suggest that accurate ventilation detection during CPR based on the proposed approach is feasible, and that the performance is not significantly degraded in presence of chest compressions.
The detection system was developed and evaluated on recordings of real-world resuscitation efforts of cardiac arrest patients where ventilations were manually annotated by human experts.
Author Risdal, Martin
Aase, Sven Ole
Stavland, Mette
Eftestol, Trygve
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  surname: Eftestol
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/18075040$$D View this record in MEDLINE/PubMed
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Snippet It has been suggested to develop automated external defibrillators with the ability to monitor cardiopulmonary resuscitation (CPR) performance online and give...
The detection system was developed and evaluated on recordings of real-world resuscitation efforts of cardiac arrest patients where ventilations were manually...
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SubjectTerms Algorithms
Cardiac arrest
Cardiography, Impedance - methods
Cardiology
Cardiopulmonary resuscitation
Cardiopulmonary Resuscitation - methods
Computerized monitoring
CPR
Defibrillators
Degradation
Diagnosis, Computer-Assisted - methods
Feedback
Humans
Impedance
Lungs
neural networks
Patient monitoring
pattern recognition
Prognosis
Pulmonary Ventilation
Reproducibility of Results
Sensitivity and Specificity
Therapy, Computer-Assisted - methods
Ventilation
Title Impedance-Based Ventilation Detection During Cardiopulmonary Resuscitation
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