Computerized STEMI recognition: an example of the art and science of building ECG algorithms

Abstract 

With the advent of thrombolytics, guidelines for ST-elevated myocardial infarction (STEMI) recognition were presented in terms of an ST segment exceeding a particular level (1 or 2 mm) in 2 contiguous leads. However, more than half of prehospital electrocardiograms that exceed these ST criteria are from patients not having an acute myocardial infarction. In contrast, expert physicians (EXMD) maintain a high specificity (>95%) for the recognition of STEMI. Likewise, in terms of increasing sensitivity, it has been found that the EXMD will classify STEMI at lower levels than specified in the guideline. Thus, the EXMD uses additional electrocardiogram features to identify patients for appropriate intervention.

Given that STEMI can be defined in terms of a pattern that is recognized by the EXMD as well as a clinical classification that can be evaluated in terms of clinical outcomes, the development and validation of a computer algorithm for STEMI need to include both the art of understanding how the human is detecting STEMI as well as the science required to develop quantified criteria based on clinical outcomes.

Evidence is presented that demonstrates that reciprocal depression is a strong indicator of STEMI versus other causes of ST elevation.

Keywords: STEMI, ST-elevated myocardial infarction, Computerized recognition, Pattern recognition, Reciprocal depression, Neural network