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

Nugent CD, Lopez JA, Smith AE, Black ND - BMC Med Inform Decis Mak (2002)

Bottom Line: 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).That is given the network design and architecture, an indication can be given as to when training should stop to obtain maximum network generalisation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Medical Informatics Research Group, Faculty of Informatics, University of Ulster at Jordanstown, BT37 0QB, Northern Ireland. cd.nugent@ulst.ac.uk

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 generalisation.

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Example of a NN trained with the early stopping method of training depicting the performance with learning and test data.
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Figure 1: Example of a NN trained with the early stopping method of training depicting the performance with learning and test data.

Mentions: By employing the 'early stopping method of training' [8] it is possible to test the NN at various stages of training on a validation data set to ensure that over-fitting is avoided. With such an approach it is usual to find that the learning performance of the NN will increase monotonically for an increasing number of epochs in the usual fashion. The validation performance increases monotonically to a maximum, then it begins to decrease gradually as the training continues. With this approach the suggested point of stopping the learning is at the maximum point on the validation curve. Figure 1 shows an example of a NN trained in this fashion. As indicated in Figure 1 the validation performance increases monotonically to a maximum, occurring at just below 500 epochs. After this point, although the learn performance continues to increase, the validation performance begins to decrease gradually. Thus by employing the early stopping method of training, a network can be trained to a point of maximal generalisation based on a validation set and thus over-fitting is avoided. Although this increases the computational requirements of the NN learning process, benefit is obtained in terms of higher levels of generalisation.


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

Nugent CD, Lopez JA, Smith AE, Black ND - BMC Med Inform Decis Mak (2002)

Example of a NN trained with the early stopping method of training depicting the performance with learning and test data.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC65522&req=5

Figure 1: Example of a NN trained with the early stopping method of training depicting the performance with learning and test data.
Mentions: By employing the 'early stopping method of training' [8] it is possible to test the NN at various stages of training on a validation data set to ensure that over-fitting is avoided. With such an approach it is usual to find that the learning performance of the NN will increase monotonically for an increasing number of epochs in the usual fashion. The validation performance increases monotonically to a maximum, then it begins to decrease gradually as the training continues. With this approach the suggested point of stopping the learning is at the maximum point on the validation curve. Figure 1 shows an example of a NN trained in this fashion. As indicated in Figure 1 the validation performance increases monotonically to a maximum, occurring at just below 500 epochs. After this point, although the learn performance continues to increase, the validation performance begins to decrease gradually. Thus by employing the early stopping method of training, a network can be trained to a point of maximal generalisation based on a validation set and thus over-fitting is avoided. Although this increases the computational requirements of the NN learning process, benefit is obtained in terms of higher levels of generalisation.

Bottom Line: 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).That is given the network design and architecture, an indication can be given as to when training should stop to obtain maximum network generalisation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Medical Informatics Research Group, Faculty of Informatics, University of Ulster at Jordanstown, BT37 0QB, Northern Ireland. cd.nugent@ulst.ac.uk

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 generalisation.

Show MeSH
Related in: MedlinePlus