Improving automatic detection of acute myocardial infarction in the presence of confounders

Several ECG features are common electrocardiographic markers for manual interpretation of early repolarization (ER) and acute pericarditis (PCARD), both confounders for acute myocardial infarction (AMI). We hypothesized these features could improve automated AMI detection in the presence of ER and PCARD. Method: The training set of ECGs included cardiologist reading of ER (n= 147), PCARD (n= 114), normal (n=239) and AMI (n=380). AMI was confirmed by reading infarct evolution in serial ECGs. The test set came from emergency department chest pain patients (n= 1806). The reference was discharge diagnosis of AMI. Positive ECGs (n=1023) were both STEMI and NSTEMI. ECGs not meeting STEMI criteria by algorithm were excluded from both the test and training sets leaving 430 and 581 ECGs respectively. Two logistic regression AMI classifiers were compared, one using traditional features, another using traditional plus additional features to help detect ER and PCARD. Additional features included J-waves, notches, slurs, PQ segment depression, ST-T concavity, spatial QRS-T angle, and T-wave PCA ratio. Results: As expected, the traditional ST-T features had the most discrimination power. However, the automatically-selected best features included T-wave PCA ratio and the mean anterior PQ segment depression. Total accuracy was higher for the additional feature classifier, 79% versus 70% . Conclusion: Additional ECG features aimed at ER and PCARD may improve automatic AMI classification when STEMI criteria are met.