  In this paper we describe ECG-SMART-NET for identification of occlusion
myocardial infarction (OMI). OMI is a severe form of heart attack characterized
by complete blockage of one or more coronary arteries requiring immediate
referral for cardiac catheterization to restore blood flow to the heart. Two
thirds of OMI cases are difficult to visually identify from a 12-lead
electrocardiogram (ECG) and can be potentially fatal if not identified in a
timely fashion. Previous works on this topic are scarce, and current
state-of-the-art evidence suggests that both random forests with engineered
features and convolutional neural networks (CNNs) are promising approaches to
improve the ECG detection of OMI. While the ResNet architecture has been
successfully adapted for use with ECG recordings, it is not ideally suited to
capture informative temporal features within each lead and the spatial
concordance or discordance across leads. We propose a clinically informed
modification of the ResNet-18 architecture. The model first learns temporal
features through temporal convolutional layers with 1xk kernels followed by a
spatial convolutional layer, after the residual blocks, with 12x1 kernels to
learn spatial features. The new ECG-SMART-NET was benchmarked against the
original ResNet-18 and other state-of-the-art models on a multisite real-word
clinical dataset that consists of 10,893 ECGs from 7,297 unique patients (rate
of OMI = 6.5%). ECG-SMART-NET outperformed other models in the classification
of OMI with a test AUC score of 0.889 +/- 0.027 and a test average precision
score of 0.587 +/- 0.087.
