Cardiac Arrhythmia Discrimination Using Evolutionary Computation

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Juan Francisco Martin-Garcia and 
                 Inmaculada Mora-Jimenez and Arcadio Garcia-Alberola and 
                 Jose Luis Rojo-Alvarez",
  title =        "Cardiac Arrhythmia Discrimination Using Evolutionary
  booktitle =    "Computing in Cardiology Conference (CinC 2014)",
  year =         "2014",
  month =        sep,
  pages =        "121--124",
  ISSN =         "2325-8861",
  URL =          "",
  size =         "4 pages",
  abstract =     "The use of Implantable Cardioverter Defibrillators
                 (ICD) for cardiac arrhythmia treatment implies a search
                 for efficiency in terms of discrimination quality and
                 computational complexity, given that improved
                 efficiency will automatically turn into more effective
                 therapy and longer battery lifetime. In this work, we
                 applied evolutionary computation to create classifiers
                 capable of discriminating between ventricular and
                 supraventricular tachycardia (VT/SVT) in episodes
                 registered by ICDs. Evolutionary computation comprises
                 several paradigms emulating natural mechanisms for
                 solving a problem, all of them characterised by a
                 population of individuals (possible solutions) which
                 evolve generation after generation to provide fitter
                 solutions. Genetic programming was the paradigm chosen
                 here because its solutions, coded as decision trees,
                 can be both computationally simple and clinically
                 interpretable. For the experiments, we considered
                 electrograms (EGM) from episodes registered by ICDs in
                 spontaneous/induced tachycardia, previously classified
                 as VT/SVT by clinical experts from several Spanish
                 healthcare centres. Training data were 38 real-valued
                 samples, arranged as the concatenation of two beat
                 segments: a sinus rhythm template immediately previous
                 to the arrhythmic episode (basal reference), and the
                 arrhythmic episode template. Several low complexity
                 trees provided low error rates and allowed
                 physiological interpretation. The best tree yielded an
                 error rate of 1.8percent, with both sensitivity and
                 specificity above 98percent. This solution compares two
                 samples from the end of the arrhythmic pulse with
                 another two samples from the sinus rhythm, pointing out
                 to a relevant discrimination role of the lasting EGM.",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Also known as \cite{7042994}",

Genetic Programming entries for Juan Francisco Martin-Garcia Inmaculada Mora-Jimenez Arcadio Garcia-Alberola Jose Luis Rojo-Alvarez