Prediction models in the design of neural network based ECG classifiers: A neural network and genetic programming approach

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Chris D. Nugent and Jesus A. Lopez and 
                 Ann E. Smith and Norman D. Black",
  title =        "Prediction models in the design of neural network
                 based ECG classifiers: A neural network and genetic
                 programming approach",
  journal =      "BMC Medical Informatics Decision Making",
  year =         "2002",
  volume =       "2",
  number =       "1",
  pages =        "1",
  month =        "11 " # jan,
  keywords =     "genetic algorithms, genetic programming, ANN",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1186/1472-6947-2-1",
  size =         "6 pages",
  abstract =     "Background Classification of the electrocardiogram
                 using Neural Networks has become a widely used method
                 in recent years. The efficiency of these classifiers
                 depends upon a number of factors including network
                 training. Unfortunately, there is a shortage of
                 evidence available to enable specific design choices to
                 be made and as a consequence, many designs are made on
                 the basis of trial and error. In this study we develop
                 prediction models to indicate the point at which
                 training should stop for Neural Network based
                 Electrocardiogram classifiers in order to ensure
                 maximum generalisation. Methods Two prediction models
                 have been presented; one based on Neural Networks and
                 the other on Genetic Programming. The inputs to the
                 models were 5 variable training parameters and the
                 output indicated the point at which training should
                 stop. Training and testing of the models was based on
                 the results from 44 previously developed bi-group
                 Neural Network classifiers, discriminating between
                 Anterior Myocardial Infarction and normal patients.
                 Results Our results show that both approaches provide
                 close fits to the training data; p = 0.627 and p =
                 0.304 for the Neural Network and Genetic Programming
                 methods respectively. For unseen data, the Neural
                 Network exhibited no significant differences between
                 actual and predicted outputs (p = 0.306) while the
                 Genetic Programming method showed a marginally
                 significant difference (p = 0.047). Conclusions The
                 approaches provide reverse engineering solutions to the
                 development of Neural Network based Electrocardiogram
                 classifiers. That is given the network design and
                 architecture, an indication can be given as to when
                 training should stop to obtain maximum network

Genetic Programming entries for Chris D Nugent Jesus A Lopez Ann E Smith Norman D Black