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 |
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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. |
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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 |
Author_xml | – sequence: 1 givenname: Martin surname: Risdal fullname: Risdal, Martin email: martin.risdal@roxar.com organization: Roxar Flow Measurements AS, PDMS, Stavanger, Norway, N-4065 – sequence: 2 givenname: Sven Ole surname: Aase fullname: Aase, Sven Ole organization: Department of Electrical and Computer Engineering, University of Stavanger, Stavanger, Norway, 4036 – sequence: 3 givenname: Mette surname: Stavland fullname: Stavland, Mette organization: Laerdal Medical AS, Stavanger, Norway, 4002 – sequence: 4 givenname: Trygve surname: Eftestol fullname: Eftestol, Trygve organization: Department of Electrical and Computer Engineering, University of Stavanger, Stavanger, Norway, 4036 |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/18075040$$D View this record in MEDLINE/PubMed |
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Cites_doi | 10.1109/TBME.2002.804591 10.1161/01.CIR.0000126594.79136.61 10.1016/0300-9572(93)90162-J 10.1016/0300-9572(94)90064-7 10.1109/ICNN.1993.298623 10.1001/jama.293.3.305 10.1001/jama.293.3.299 10.1161/CIRCULATIONAHA.105.166553 10.1001/jama.274.24.1922 10.1097/01.CCM.0000235666.40378.60 10.1109/10.983461 10.1109/TBME.2002.803514 |
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References | ref12 ref15 ref11 ref10 losert (ref14) 2004 ref2 ref1 risdal (ref17) 2005; 11 baker (ref9) 1989 ref16 therrien (ref13) 1992 ref8 (ref3) 2000; 102 ref7 ref4 ref6 ref5 duda (ref18) 2000 |
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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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