A support vector machine for predicting defibrillation outcomes from waveform metrics.

@article{Howe2014ASV,
  title={A support vector machine for predicting defibrillation outcomes from waveform metrics.},
  author={Andrew Howe and Omar J. Escalona and Rebecca Di Maio and Bertrand Massot and Nick A. Cromie and Karen Darragh and Jennifer A A Adgey and David J. McEneaney},
  journal={Resuscitation},
  year={2014},
  volume={85 3},
  pages={343-9}
}
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… CONTINUE READING
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