Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2014 (2014), Article ID 276965, 19 pages
http://dx.doi.org/10.1155/2014/276965
Research Article

Towards the Automated Analysis and Database Development of Defibrillator Data from Cardiac Arrest

1Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Stavanger, 4036 Stavanger, Norway
2Department of Medicine, University of Washington, 999 3rd Avenue, Suite 700, Seattle, WA 98104, USA
3Department of Bioengineering, University of Washington, 999 3rd Avenue, Suite 700, Seattle, WA 98104, USA

Received 4 October 2013; Accepted 22 November 2013; Published 12 January 2014

Academic Editor: Giuseppe Ristagno

Copyright © 2014 Trygve Eftestøl and Lawrence D. Sherman. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background. During resuscitation of cardiac arrest victims a variety of information in electronic format is recorded as part of the documentation of the patient care contact and in order to be provided for case review for quality improvement. Such review requires considerable effort and resources. There is also the problem of interobserver effects. Objective. We show that it is possible to efficiently analyze resuscitation episodes automatically using a minimal set of the available information. Methods and Results. A minimal set of variables is defined which describe therapeutic events (compression sequences and defibrillations) and corresponding patient response events (annotated rhythm transitions). From this a state sequence representation of the resuscitation episode is constructed and an algorithm is developed for reasoning with this representation and extract review variables automatically. As a case study, the method is applied to the data abstraction process used in the King County EMS. The automatically generated variables are compared to the original ones with accuracies for 18 variables and for the remaining four variables. Conclusions. It is possible to use the information present in the CPR process data recorded by the AED along with rhythm and chest compression annotations to automate the episode review.