Neural networks--a diagnostic tool in acute myocardial infarction with concomitant left bundle branch block.
(2002) In Clinical Physiology and Functional Imaging 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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/110480
- author
- Olsson, Sven-Erik LU ; Ohlsson, Mattias LU ; Öhlin, Hans LU and Edenbrandt, Lars LU
- organization
- publishing date
- 2002
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Clinical Physiology and Functional Imaging
- volume
- 22
- issue
- 4
- pages
- 295 - 299
- publisher
- John Wiley & Sons Inc.
- 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
- 2016-04-01 12:15:41
- date last changed
- 2024-01-08 14:07:34
@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 = {{John Wiley & Sons Inc.}}, series = {{Clinical Physiology and Functional Imaging}}, 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}}, doi = {{10.1046/j.1475-097X.2002.00433.x}}, volume = {{22}}, year = {{2002}}, }