title: A support vector machine for predicting defibrillation outcomes from waveform metrics.
abstract: BACKGROUND
  Algorithms to predict shock success based on VF waveform metrics could significantly enhance resuscitation by optimising the timing of defibrillation.
  
  
  OBJECTIVE
  To investigate robust methods of predicting defibrillation success in VF cardiac arrest patients, by using a support vector machine (SVM) optimisation approach.
  
  
  METHODS
  Frequency-domain (AMSA, dominant frequency and median frequency) and time-domain (slope and RMS amplitude) VF waveform metrics were calculated in a 4.1Y window prior to defibrillation. Conventional prediction test validity of each waveform parameter was conducted and used AUC>0.6 as the criterion for inclusion as a corroborative attribute processed by the SVM classification model. The latter used a Gaussian radial-basis-function (RBF) kernel and the error penalty factor C was fixed to 1. A two-fold cross-validation resampling technique was employed.
  
  
  RESULTS
  A total of 41 patients had 115 defibrillation instances. AMSA, slope and RMS waveform metrics performed test validation with AUC>0.6 for predicting termination of VF and return-to-organised rhythm. Predictive accuracy of the optimised SVM design for termination of VF was 81.9% (± 1.24 SD); positive and negative predictivity were respectively 84.3% (± 1.98 SD) and 77.4% (± 1.24 SD); sensitivity and specificity were 87.6% (± 2.69 SD) and 71.6% (± 9.38 SD) respectively.
  
  
  CONCLUSIONS
  AMSA, slope and RMS were the best VF waveform frequency-time parameters predictors of termination of VF according to test validity assessment. This a priori can be used for a simplified SVM optimised design that combines the predictive attributes of these VF waveform metrics for improved prediction accuracy and generalisation performance without requiring the definition of any threshold value on waveform metrics.
authors.affiliation: Cardiovascular Research Unit, Craigavon Area Hospital, Portadown, UK.
authors.forename: Andrew
authors.initials: A
authors.lastname: Howe
authors.affiliation: Centre for Advanced Cardiovascular Research (CACR), University of Ulster, Newtownabbey, UK. Electronic address: oj.escalona@ulster.ac.uk.
authors.forename: Omar J
authors.initials: OJ
authors.lastname: Escalona
authors.affiliation: Heartsine Technologies Ltd, Belfast, UK.
authors.forename: Rebecca
authors.initials: R
authors.lastname: Di Maio
authors.affiliation: Institut des Nanotechnologies de Lyon, INSA-Lyon, Villeurbanne, France.
authors.forename: Bertrand
authors.initials: B
authors.lastname: Massot
authors.affiliation: Belfast Heart Centre, Royal Victoria Hospital, Belfast, UK.
authors.forename: Nick A
authors.initials: NA
authors.lastname: Cromie
authors.affiliation: Belfast Heart Centre, Royal Victoria Hospital, Belfast, UK.
authors.forename: Karen M
authors.initials: KM
authors.lastname: Darragh
authors.affiliation: Belfast Heart Centre, Royal Victoria Hospital, Belfast, UK.
authors.forename: Jennifer
authors.initials: J
authors.lastname: Adgey
authors.affiliation: Cardiovascular Research Unit, Craigavon Area Hospital, Portadown, UK.
authors.forename: David J
authors.initials: DJ
authors.lastname: McEneaney
mesh_terms: D004554:Electric Countershock; D005260:Female; D006323:Heart Arrest / Q000150:complications / Q000503:physiopathology* / Q000628:therapy*; D006801:Humans; D008297:Male; D008875:Middle Aged; D011379:Prognosis; D012189:Retrospective Studies; D060388:Support Vector Machine; D016896:Treatment Outcome; D014693:Ventricular Fibrillation / Q000150:complications / Q000503:physiopathology / Q000628:therapy
keywords: CPR; Cardiac arrest; Defibrillation; Resuscitation therapy; Support vector machine (SVM); VF waveform metrics