USING DIRECTLY ACQUIRED DIGITAL ECG DATA TO OPTIMIZE THE DIAGNOSTIC-CRITERIA FOR ANTERIOR MYOCARDIAL-INFARCTION

The use of a newly developed method of directly transferring digital data from Marquette electrocardiogram (EGG) systems (Milwaukee, Wisconsin) to personal computers for subsequent storage and analysis is illustrated. This method can eliminate the slowness and inaccuracy associated with measuring relevant ECG parameters from analog tracings and manually entering the data into a computer. In this study, the new method was used to derive ECG criteria for anterior myocardial infarction and to compare their performances to those of the current Marquette 12SL diagnostic program and of a group of cardiologists who had also interpreted the ECGs. Using angiographic data, 82 normal subjects and 55 patients with anterior myocardial infarction were identified. The digital ECG data from the patients in each group were transferred to a personal computer and frequency distributions of these data were generated. From these frequency distributions, the ECG criteria that most reliably separated the two groups were identified. The diagnostic performance of the best of these empirically derived criteria appears clinically superior to the performances of both the 1ZSL program and the cardiologists who had also interpreted the ECGs.