Neural network versus electrocardiographer and conventional computer criteria in diagnosing anterior infarct from the ECG

The purpose of the present study was to examine the performance of a neural network in an electrocardiogram (ECG) classification task. ECGs recorded from 272 patients with anterior myocardial infarction and 479 subjects without myocardial infarction were studied. Fifteen QRS measurements of the leads V2-V4 were used as inputs to the network. The network was trained using 502 ECGs. Thereafter, a comparison of the network, conventional criteria and a human expert was performed using a test set of 249 ECGs. The neural network showed a higher sensitivity than the conventional criteria, both having a specificity of 97%. The performance of the human expert was the same as that of the neural network. It seems that neural networks could be used to improve the performance of some parts of ECG interpretation programs.<
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