Identification of best electrocardiographic leads for diagnosing myocardial infarction by statistical analysis of body surface potential maps.

This study describes a practical approach for the extraction of diagnostic information from body surface potential maps. Body surface potential map data from 361 subjects were used to identify optimal subsets of leads and features to distinguish 184 normal subjects from 177 patients with myocardial infarction (MI). Multivariate analysis was performed on 120-lead data, using as features instantaneous voltage measurements on time-normalized QRS and STT waveforms. Several areas on the map, most of which were located outside the precordial region, contained leads with important discriminant features; 2 of the 3 limb leads (aVR and aVF) also exhibited high diagnostic capability. A total of 6 features (mostly STT measurements) from 3 locations accounted for a specificity of 95% and a sensitivity of 95%; these were the right subclavicular area, the left posterior axillary region and the left leg. As a comparison, the same number of features from the standard 12-lead electrocardiogram yielded a sensitivity of 88% for a specificity of 95%. To investigate the repeatability of the results, the entire population was separated into a training set (100 normal subjects and 100 patients with MI) and a testing set (84 normal subjects and 77 patients with MI); computing a discriminant function on the training set and applying it to the testing set only moderately deteriorated the diagnostic classification. It is concluded that this approach achieves efficient information extraction from body surface potential maps for improved diagnostic classification.