  Myocardial infarction is the leading cause of death worldwide. In this paper,
we design domain-inspired neural network models to detect myocardial
infarction. First, we study the contribution of various leads. This systematic
analysis, first of its kind in the literature, indicates that out of 15 ECG
leads, data from the v6, vz, and ii leads are critical to correctly identify
myocardial infarction. Second, we use this finding and adapt the ConvNetQuake
neural network model--originally designed to identify earthquakes--to attain
state-of-the-art classification results for myocardial infarction, achieving
$99.43\%$ classification accuracy on a record-wise split, and $97.83\%$
classification accuracy on a patient-wise split. These two results represent
cardiologist-level performance level for myocardial infarction detection after
feeding only 10 seconds of raw ECG data into our model. Third, we show that our
multi-ECG-channel neural network achieves cardiologist-level performance
without the need of any kind of manual feature extraction or data
pre-processing.
