Usefulness of serial electrocardiograms for diagnosis of acute myocardial infarction.

The purpose of this study was to determine whether the automated detection of acute myocardial infarction (AMI) by utilizing artificial neural networks was improved by using a previous electrocardiogram (ECG) in addition to the current ECG. A total of 4,691 ECGs were recorded from patients admitted to an emergency department due to suspected AMI. Of these, 902 ECGs, in which diagnoses of AMI were later confirmed, formed the study group, whereas the remaining 3,789 ECGS comprised the control group. For each ECG recorded, a previous ECG of the same patient was selected from the clinical electrocardiographic database. Artificial neural networks were then programmed to detect AMI based on either the current ECG only or on the combination of the previous and the current ECGs. On this basis, 3 assessors--a neural network, an experienced cardiologist, and an intern--separately classified the ECGs of the test group, with and without access to the previous ECG. The detection performance, as measured by the area under the receiver operating characteristic curve, showed an increase for all assessors with access to previous ECGs. The neural network improved from 0.85 to 0.88 (p = 0.02), the cardiologist from 0.79 to 0.81 (p = 0.36), and the intern from 0.71 to 0.78 (p <0.001). Thus, the performance of a neural network, detecting AMI in an ECG, is improved when a previous ECG is used as an additional input.