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Neural networks – a diagnostic tool in acute myocardial infarction with concomitant left bundle branch block
Author(s) -
Olsson SvenErik,
Ohlsson Mattias,
Öhlin Hans,
Edenbrandt Lars
Publication year - 2002
Publication title -
clinical physiology and functional imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.608
H-Index - 67
eISSN - 1475-097X
pISSN - 1475-0961
DOI - 10.1046/j.1475-097x.2002.00433.x
Subject(s) - medicine , left bundle branch block , cardiology , myocardial infarction , concomitant , qrs complex , electrocardiography , bundle branch block , revascularization , artificial neural network , heart failure , artificial intelligence , computer science
Summary 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.