title: Beyond ventricular fibrillation analysis: comprehensive waveform analysis for all cardiac rhythms occurring during resuscitation.
abstract: OBJECTIVE
  To propose a method which analyses the electrocardiogram (ECG) waveform of any cardiac rhythm occurring during resuscitation and computes the probability of that rhythm converting into another with better prognosis (Pdes).
  
  
  METHODS
  Rhythm transitions occurring spontaneously or due to defibrillation were analyzed. For each possible rhythm, ventricular fibrillation/ventricular tachycardia (VF/VT), pulseless electrical activity (PEA), pulse-generating rhythm (PR) and asystole (AS), the desired and undesired transitions were defined. ECG segments corresponding to the last 3s of rhythms prior to transition were used to extract waveform features. For each rhythm type, waveform features were combined into a logistic regression model to develop a rhythm specific classifier of desired transitions. This model was the monitoring function for the Pdes. The capacity of each rhythm specific classifier to discriminate between desired and undesired transitions was evaluated in terms of area under the curve (AUC). Pdes was integrated into a state sequence representation, which structures the information of cardiac arrest episodes, to analyze the effect of therapy on patient. As a case study, the effect of optimal/suboptimal cardiopulmonary resuscitation (CPR) on Pdes was analyzed. The mean Pdes was computed for the pre- and post-CPR intervals which presented the same underlying rhythm. The relationship between the optimal/suboptimal CPR and increase/decrease of Pdes was analyzed.
  
  
  RESULTS
  The AUC was 0.80, 0.79, 0.73 and 0.61 for VF/VT, PEA, PR and AS respectively. The Pdes quantified the probability of every rhythm of the episode developing to a better state, and the evolution of Pdes was coherent with the provided therapy. The case study indicated, for most rhythms, that positive trends in the dynamic behaviour could be associated with optimal CPR, whereas the opposite seemed true for negative trends.
  
  
  CONCLUSIONS
  A method for continuous ECG waveform analysis covering all cardiac rhythms during resuscitation has been proposed. This methodology can be further developed to be used in retrospective studies of CPR techniques, and, in the future, for potentially monitoring in real time the probability of survival of patients being resuscitated.
authors.affiliation: Department of Electrical Engineering and Computer Science, University of Stavanger, 4036 Stavanger, Norway; Communications Engineering Department, University of the Basque Country UPV/EHU, Alameda Urquijo S/N, 48013 Bilbao, Spain. Electronic address: erik_alonso@ehu.es.
authors.forename: Erik
authors.initials: E
authors.lastname: Alonso
authors.affiliation: Department of Electrical Engineering and Computer Science, University of Stavanger, 4036 Stavanger, Norway.
authors.forename: Trygve
authors.initials: T
authors.lastname: Eftestøl
authors.affiliation: Communications Engineering Department, University of the Basque Country UPV/EHU, Alameda Urquijo S/N, 48013 Bilbao, Spain.
authors.forename: Elisabete
authors.initials: E
authors.lastname: Aramendi
authors.affiliation: Norwegian National Advisory Unit on Prehospital Emergency Medicine (NAKOS), Oslo University Hospital and University of Oslo, N-0424 Oslo, Norway.
authors.forename: Jo
authors.initials: J
authors.lastname: Kramer-Johansen
authors.affiliation: Institute for Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), N-7491 Trondheim, Norway; Department of Anesthesia and Intensive Care Medicine, St. Olav University Hospital, N-7014 Trondheim, Norway.
authors.forename: Eirik
authors.initials: E
authors.lastname: Skogvoll
authors.affiliation: Institute for Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), N-7491 Trondheim, Norway; Department of Anesthesia and Intensive Care Medicine, St. Olav University Hospital, N-7014 Trondheim, Norway.
authors.forename: Trond
authors.initials: T
authors.lastname: Nordseth
mesh_terms: D000328:Adult; D000368:Aged; D016887:Cardiopulmonary Resuscitation / Q000009:adverse effects / Q000379:methods*; D015331:Cohort Studies; D004562:Electrocardiography; D005260:Female; D005500:Follow-Up Studies; D006323:Heart Arrest / Q000175:diagnosis / Q000401:mortality / Q000628:therapy*; D006801:Humans; D015994:Incidence; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D008991:Monitoring, Physiologic / Q000379:methods; D012372:ROC Curve; D012189:Retrospective Studies; D012720:Severity of Illness Index; D017180:Tachycardia, Ventricular / Q000175:diagnosis* / Q000453:epidemiology; D013997:Time Factors; D016896:Treatment Outcome; D014693:Ventricular Fibrillation / Q000175:diagnosis* / Q000453:epidemiology
keywords: Cardiac arrest; ECG waveform analysis; Electrocardiogram (ECG); Rhythm transition classifier