ACUTE MYOCARDIAL-INFARCTION DETECTED IN THE 12-LEAD ECG BY ARTIFICIALNEURAL NETWORKS

Background The 12-lead ECG, together with patient history and clinical findings, remains the most important method for early diagnosis of ac ute myocardial infarction. Automated interpretation of ECG is widely u sed as decision support for less experienced physicians. Recent report s have demonstrated that artificial neural networks can be used to imp rove selected aspects of conventional rule-based interpretation progra ms. The purpose of this study was to detect acute myocardial infarctio n in the 12-lead ECG with artificial neural networks. Methods and Resu lts A total of 1120 ECGs from patients with acute myocardial infarctio n and 10 452 control ECGs, recorded at an emergency department with co mputerized ECGs, were studied. Artificial neural networks were trained to detect acute myocardial infarction by use of measurements from the 12 ST-T segments of each EGG, together with the correct diagnosis. Af ter this training process, the performance of the neural networks was compared with that of a widely used ECG interpretation program and the classification of an experienced cardiologist. The neural networks sh owed higher sensitivities and discriminant power than both the interpr etation program and cardiologist. The sensitivity of the neural networ ks was 15.5% (95% confidence interval [CI], 12.4 to 18.6) higher than that of the interpretation program compared at a specificity of 95.4% (P<.00001) and 10.5% (95% CI, 7.2 to 13.6) higher than the cardiologis t at a specificity of 86.3% (P<.00001). Conclusions Artificial neural networks can be used to improve automated ECG interpretation for acute myocardial infarction. The networks may be useful as decision support even for the experienced ECG readers.