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Neural networks--a diagnostic tool in acute myocardial infarction with concomitant left bundle branch block.

Olsson, Sven-Erik LU ; Ohlsson, Mattias LU ; Öhlin, Hans LU and Edenbrandt, Lars LU (2002) In Clinical Physiology and Functional Imaging2002-01-01+01:00 22(4). p.295-299
Abstract
The prognosis of acute myocardial infarction (AMI) improves by early revascularization. However the presence of left bundle branch block (LBBB) in the electrocardiogram (ECG) increases the difficulty in recognizing an AMI and different ECG criteria for the diagnosis of AMI have proved to be of limited value. The purpose of this study was to detect AMI in ECGs with LBBB using artificial neural networks and to compare the performance of the networks to that of six sets of conventional ECG criteria and two experienced cardiologists. A total of 518 ECGs, recorded at an emergency department, with a QRS duration > 120 ms and an LBBB configuration, were selected from the clinical ECG database. Of this sample 120 ECGs were recorded on patients... (More)
The prognosis of acute myocardial infarction (AMI) improves by early revascularization. However the presence of left bundle branch block (LBBB) in the electrocardiogram (ECG) increases the difficulty in recognizing an AMI and different ECG criteria for the diagnosis of AMI have proved to be of limited value. The purpose of this study was to detect AMI in ECGs with LBBB using artificial neural networks and to compare the performance of the networks to that of six sets of conventional ECG criteria and two experienced cardiologists. A total of 518 ECGs, recorded at an emergency department, with a QRS duration > 120 ms and an LBBB configuration, were selected from the clinical ECG database. Of this sample 120 ECGs were recorded on patients with AMI, the remaining 398 ECGs being used as a control group. Artificial neural networks of feed-forward type were trained to classify the ECGs as AMI or not AMI. The neural network showed higher sensitivities than both the cardiologists and the criteria when compared at the same levels of specificity. The sensitivity of the neural network was 12% (P = 0.02) and 19% (P = 0.001) higher than that of the cardiologists. Artificial neural networks can be trained to detect AMI in ECGs with concomitant LBBB more effectively than conventional ECG criteria or experienced cardiologists. (Less)
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type
Contribution to journal
publication status
published
subject
in
Clinical Physiology and Functional Imaging2002-01-01+01:00
volume
22
issue
4
pages
295 - 299
publisher
Wiley Online Library
external identifiers
  • wos:000176887900009
  • pmid:12402453
  • scopus:0035996404
ISSN
1475-0961
DOI
10.1046/j.1475-097X.2002.00433.x
language
English
LU publication?
yes
id
2e877f88-3e7a-4f5b-b3c3-db3a4cd74fed (old id 110480)
alternative location
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=12402453&dopt=Abstract
date added to LUP
2007-07-02 12:06:42
date last changed
2017-01-01 04:59:10
@article{2e877f88-3e7a-4f5b-b3c3-db3a4cd74fed,
  abstract     = {The prognosis of acute myocardial infarction (AMI) improves by early revascularization. However the presence of left bundle branch block (LBBB) in the electrocardiogram (ECG) increases the difficulty in recognizing an AMI and different ECG criteria for the diagnosis of AMI have proved to be of limited value. The purpose of this study was to detect AMI in ECGs with LBBB using artificial neural networks and to compare the performance of the networks to that of six sets of conventional ECG criteria and two experienced cardiologists. A total of 518 ECGs, recorded at an emergency department, with a QRS duration > 120 ms and an LBBB configuration, were selected from the clinical ECG database. Of this sample 120 ECGs were recorded on patients with AMI, the remaining 398 ECGs being used as a control group. Artificial neural networks of feed-forward type were trained to classify the ECGs as AMI or not AMI. The neural network showed higher sensitivities than both the cardiologists and the criteria when compared at the same levels of specificity. The sensitivity of the neural network was 12% (P = 0.02) and 19% (P = 0.001) higher than that of the cardiologists. Artificial neural networks can be trained to detect AMI in ECGs with concomitant LBBB more effectively than conventional ECG criteria or experienced cardiologists.},
  author       = {Olsson, Sven-Erik and Ohlsson, Mattias and Öhlin, Hans and Edenbrandt, Lars},
  issn         = {1475-0961},
  language     = {eng},
  number       = {4},
  pages        = {295--299},
  publisher    = {Wiley Online Library},
  series       = {Clinical Physiology and Functional Imaging2002-01-01+01:00},
  title        = {Neural networks--a diagnostic tool in acute myocardial infarction with concomitant left bundle branch block.},
  url          = {http://dx.doi.org/10.1046/j.1475-097X.2002.00433.x},
  volume       = {22},
  year         = {2002},
}