ARTIFICIAL NEURAL NETWORKS FOR THE ELECTROCARDIOGRAPHIC DIAGNOSIS OF HEALED MYOCARDIAL-INFARCTION

Artificial neural networks are computer-based expert systems that lear n by example, in contrast to the currently used rule-based electrocard iographic interpretation programs. For the purpose of this study, 1,10 7 electrocardiograms (ECGs) from patients who had undergone cardiac ca theterization were used to train and test neural networks for the diag nosis of myocardial infarction. Different combinations of QRS and ST-T measurements were used as input to the neural networks. In a learning process, the networks automatically adjusted their characteristics to correctly diagnose anterior or inferior wall myocardial infarction fr om the ECG. Two thirds of the ECGs were used in this process. Thereaft er, the performance of the networks was studied in a separate test set , using the remaining third of the ECGs. The results from the networks were also compared with that of conventional electrocardiographic cri teria. Tbe sensitivity for the diagnosis of anterior myocardial infarc tion was 81% for the best network and 68% for the conventional criteri a (p <0.01); both having a specificity of 97.5%. The corresponding sen sitivities of the network and the criteria for the diagnosis of inferi or myocardial infarction were 78% and 65.5% (p <0.01), respectively, c ompared at a Specificity of 95%. The results indicate that artificial neural networks may be of interest in the attempt to improve computer- based electrocardiographic interpretation programs.