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A confident decision support system for interpreting electrocardiograms

Holst, Holger LU ; Ohlsson, Mattias LU ; Peterson, Carsten LU and Edenbrandt, Lars LU (1999) In Clinical Physiology 19(5). p.410-418
Abstract

Computer-aided interpretation of electrocardiograms (ECGs) is widespread but many physicians hesitate to rely on the computer, because the advice is presented without information about the confidence of the advice. The purpose of this work was to develop a method to validate the advice of a computer by estimating the error of an artificial neural network output. A total of 1249 ECGs, recorded with computerized electrocardiographs, on patients who had undergone diagnostic cardiac catheterization were studied. The material consisted of two groups, 414 patients with and 835 without anterior myocardial infarction. The material was randomly divided into three data sets. The first set was used to train an artificial neural network for the... (More)

Computer-aided interpretation of electrocardiograms (ECGs) is widespread but many physicians hesitate to rely on the computer, because the advice is presented without information about the confidence of the advice. The purpose of this work was to develop a method to validate the advice of a computer by estimating the error of an artificial neural network output. A total of 1249 ECGs, recorded with computerized electrocardiographs, on patients who had undergone diagnostic cardiac catheterization were studied. The material consisted of two groups, 414 patients with and 835 without anterior myocardial infarction. The material was randomly divided into three data sets. The first set was used to train an artificial neural network for the diagnosis of anterior infarction. The second data set was used to calculate the error of the network outputs. The last data set was used to test the network performance and to estimate the error of the network outputs. The performance of the neural network, measured as the area under the receiver operating characteristic (ROC) curve, was 0.887 (0.845-0.922). The 25% test ECGs with the lowest error estimates had an area under the ROC curve as high as 0.995 (0.982-1.000), i.e. almost all of these ECGs were correctly classified. Neural networks can therefore be trained to diagnose myocardial infarction and to signal when the advice is given with great confidence or when it should be considered more carefully. This method increases the possibility that artificial neural networks will be accepted as reliable decision support systems in clinical practice.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Computer-assisted electrocardiography, Diagnosis, Myocardial infarction, Neural networks (computer)
in
Clinical Physiology
volume
19
issue
5
pages
9 pages
publisher
Wiley-Blackwell
external identifiers
  • scopus:0032878967
ISSN
0144-5979
DOI
10.1046/j.1365-2281.1999.00195.x
language
English
LU publication?
yes
id
2b0b78a0-e48d-4cca-830c-131c275a82f6
date added to LUP
2017-05-19 08:12:57
date last changed
2017-05-22 09:06:31
@article{2b0b78a0-e48d-4cca-830c-131c275a82f6,
  abstract     = {<p>Computer-aided interpretation of electrocardiograms (ECGs) is widespread but many physicians hesitate to rely on the computer, because the advice is presented without information about the confidence of the advice. The purpose of this work was to develop a method to validate the advice of a computer by estimating the error of an artificial neural network output. A total of 1249 ECGs, recorded with computerized electrocardiographs, on patients who had undergone diagnostic cardiac catheterization were studied. The material consisted of two groups, 414 patients with and 835 without anterior myocardial infarction. The material was randomly divided into three data sets. The first set was used to train an artificial neural network for the diagnosis of anterior infarction. The second data set was used to calculate the error of the network outputs. The last data set was used to test the network performance and to estimate the error of the network outputs. The performance of the neural network, measured as the area under the receiver operating characteristic (ROC) curve, was 0.887 (0.845-0.922). The 25% test ECGs with the lowest error estimates had an area under the ROC curve as high as 0.995 (0.982-1.000), i.e. almost all of these ECGs were correctly classified. Neural networks can therefore be trained to diagnose myocardial infarction and to signal when the advice is given with great confidence or when it should be considered more carefully. This method increases the possibility that artificial neural networks will be accepted as reliable decision support systems in clinical practice.</p>},
  author       = {Holst, Holger and Ohlsson, Mattias and Peterson, Carsten and Edenbrandt, Lars},
  issn         = {0144-5979},
  keyword      = {Computer-assisted electrocardiography,Diagnosis,Myocardial infarction,Neural networks (computer)},
  language     = {eng},
  number       = {5},
  pages        = {410--418},
  publisher    = {Wiley-Blackwell},
  series       = {Clinical Physiology},
  title        = {A confident decision support system for interpreting electrocardiograms},
  url          = {http://dx.doi.org/10.1046/j.1365-2281.1999.00195.x},
  volume       = {19},
  year         = {1999},
}