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

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 ≥90% for 18 variables and ≥85% 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.

An automatic system for the comprehensive retrospective analysis of cardiac rhythms in resuscitation episodes

“…Furthermore, we demonstrate and evaluate the accuracy of the system on a comprehensive dataset of clinically annotated complete resuscitation episodes. This architecture integrates a body of knowledge developed over the last decade in signal processing applied to 4 resuscitation data annotation, in line with the general annotation framework proposed by Eftestøl et al 1 for the comprehensive analysis of resuscitation data.…”

“…The annotation of cardiac rhythms in full-length resuscitation episodes would contribute to a richer retrospective analysis of resuscitation data and to a better understanding of the interplay between therapy and patient response. 1 It could help to determine optimal chest compression strategies, a better understanding of the effects of chest compression pauses and their duration, or to maximize the likelihood of successful defibrillation attempts. [2][3][4][5][6][7] To date, cardiac rhythm classification and the identification of rhythm transitions with and without chest compression artefacts have been done manually by expert clinicians.…”