Using a neural network to diagnose anterior wall myocardial infarction

The purpose of this study is to evaluate the usefulness of a feedforward neural network trained by the backpropagation algorithm for diagnosing anterior wall myocardial infarction (AI). Data used in the study are 165 ECG records. These ECG were all diagnosed as old AI by the commercially available computer-assisted ECG interpretation system (FCP4301), but only 80 of 165 cases have been proved to suffer from AI, the other 85 cases have been proved without AI by history, physical examinations, echocardiography and laboratory test results. The training set is composed of 40 ECG randomly selected from the 80 AI cases and 42 ECG randomly selected from the 85 cases without AI. The performance of the network was tested with the remaining 83 ECG data; 40 with AI and 43 without it. The testing data had not been exposed to the training network. The network correctly diagnosed 34 of the 40 cases with AI and 39 of the 43 cases without AI. The sensitivity and the specificity were 85% and 90.7%, respectively, and the diagnostic accuracy rate was 87.9%. The good diagnostic accuracy rate revealed the network has the potential to improve computer-assisted interpretation of EGG