title: Automatic cardiac rhythm interpretation during resuscitation.
abstract: OBJECTIVE
  Resuscitation guidelines recommend different treatments depending on the patient's cardiac rhythm. Rhythm interpretation is a key tool to retrospectively evaluate and improve the quality of treatment. Manual rhythm annotation is time consuming and an obstacle for handling large resuscitation datasets efficiently. The objective of this study was to develop a system for automatic rhythm interpretation by using signal processing and machine learning algorithms.
  
  
  METHODS
  Data from 302 out of hospital cardiac arrest patients were used. In total 1669 3-second artifact free ECG segments with clinical rhythm annotations were extracted. The proposed algorithms combine 32 features obtained from both wavelet- and time-domain representations of the ECG, followed by a feature selection procedure based on the wrapper method in a nested cross-validation architecture. Linear and quadratic discriminant analyses (LDA and QDA) were used to automatically classify the segments into one of five rhythm types: ventricular tachycardia (VT), ventricular fibrillation (VF), pulseless electrical activity (PEA), asystole (AS), and pulse generating rhythms (PR).
  
  
  RESULTS
  The overall accuracy for the best algorithm was 68%. VT, VF, and AS are recognized with sensitivities of 71%, 75%, and 79%, respectively. Sensitivities for PEA and PR were 55% and 56%, respectively, which reflects the difficulty of identifying pulse using only the ECG.
  
  
  CONCLUSIONS
  An ECG based automatic rhythm interpreter for resuscitation has been demonstrated. The interpreter handles VT, VF and AS well, while PEA and PR discrimination poses a more difficult problem.
authors.affiliation: Department of Electrical Engineering and Computer Science, University of Stavanger, 4036 Stavanger, Norway; Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL 60208, USA. Electronic address: ali.bahrami.rad@uis.no.
authors.forename: Ali Bahrami
authors.initials: AB
authors.lastname: Rad
authors.affiliation: Department of Electrical Engineering and Computer Science, University of Stavanger, 4036 Stavanger, Norway.
authors.forename: Kjersti
authors.initials: K
authors.lastname: Engan
authors.affiliation: Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL 60208, USA.
authors.forename: Aggelos K
authors.initials: AK
authors.lastname: Katsaggelos
authors.affiliation: Department of Mathematics and Natural Sciences, University of Stavanger, 4036 Stavanger, Norway.
authors.forename: Jan Terje
authors.initials: JT
authors.lastname: Kvaløy
authors.affiliation: Norwegian National Advisory Unit on Prehospital Emergency Medicine (NAKOS) and Department of Anaesthesiology, Oslo University Hospital and University of Oslo, Pb 4956 Nydalen, 0424 Oslo, Norway.
authors.forename: Lars
authors.initials: L
authors.lastname: Wik
authors.affiliation: Norwegian National Advisory Unit on Prehospital Emergency Medicine (NAKOS) and Department of Anaesthesiology, Oslo University Hospital and University of Oslo, Pb 4956 Nydalen, 0424 Oslo, Norway.
authors.forename: Jo
authors.initials: J
authors.lastname: Kramer-Johansen
authors.affiliation: Communications Engineering Department, University of the Basque Country UPV/EHU, Alameda Urquijo S/N, 48013 Bilbao, Spain.
authors.forename: Unai
authors.initials: U
authors.lastname: Irusta
authors.affiliation: Department of Electrical Engineering and Computer Science, University of Stavanger, 4036 Stavanger, Norway.
authors.forename: Trygve
authors.initials: T
authors.lastname: Eftestøl
mesh_terms: D000368:Aged; D000465:Algorithms; D001331:Automation / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D008875:Middle Aged; D058687:Out-of-Hospital Cardiac Arrest / Q000503:physiopathology* / Q000628:therapy; D015203:Reproducibility of Results; D012151:Resuscitation; D012189:Retrospective Studies
keywords: Cardiac rhythm interpretation; Cardiopulmonary resuscitation; Classification; Feature extraction; Feature selection