Open Access Repository

Application of neural network classifers to electrocardiographic body surface mapping


Downloads per month over past year

Freeman, Timothy William 1998 , 'Application of neural network classifers to electrocardiographic body surface mapping', Research Master thesis, University of Tasmania.

PDF (Whole thesis)
whole_FreemanTi...pdf | Download (27MB)
Available under University of Tasmania Standard License.

| Preview


This thesis examines the capabilities of artificial neural networks for classifying
electrocardiographic body surface mapping data. In particular it examines the
diagnostic detection of myocardial infarctions and coronary artery disease. An
overview of patten recognition, neural networks, electrocardiography, and
electrocardiographic body surface mapping is presented followed by a detailed
description of the experiments and analysis conducted.
The experimental analysis in this thesis is divided into three sections. Firstly, a range of
feed-forward artificial neural network architectures and training techniques are used to
classify the body surface mapping data with the aim of identifying patients with
myocardial infarctions, coronary artery disease, and normal heart function. In this
initial study a number of pre-processing techniques are also explored.
Secondly, a range of traditional classification techniques (linear regression, k-nearest-neighbour,
and inductive learning) are applied to the same problems and compared with
the neural network results. When classifying myocardial infarction it was found that
artificial neural networks perform as well but no better than traditional classification
techniques. This outcome provides some interesting insights into the nature of the
classification problem and the information content of body surface maps. However,
attempting to separate patients with coronary artery disease from patients with normal
heart function neural networks were found to perform much better than traditional
classification techniques.
The third experimental section examines the bayesian equivalence of neural network
outputs and how these probabilistic properties may be used to deal with diagnostic
uncertainty. Apart from examining the theoretical connection between network outputs
and a posteriori probabilities, a number of experiments are conducted to show how this
information can be used to provide the physician with some important information
about the classification certainty.

Item Type: Thesis - Research Master
Authors/Creators:Freeman, Timothy William
Keywords: Body surface mapping, Electrocardiography, Heart, Coronary heart disease
Copyright Holders: The Author
Copyright Information:

Copyright 1998 the Author - The University is continuing to endeavour to trace the copyright
owner(s) and in the meantime this item has been reproduced here in good faith. We
would be pleased to hear from the copyright owner(s).

Additional Information:

Available for library use only and copying in accordance with the Copyright Act 1968, as amended. Thesis (MSc)--University of Tasmania, 1998. Includes bibliographical references. 1. Introduction -- 2. Pattern recognition -- 3. Neural networks -- 4. The heart and standard electrocardiography -- 5. Electocardiographic body surface mapping -- 6. Experimental design -- 7. Application of multilayer perceptrons to BSM data classification -- 8. Alternative classification techniques applied to BSM data classification -- 9. Improving classification reliability -- Conclusion

Item Statistics: View statistics for this item

Actions (login required)

Item Control Page Item Control Page