Clinically-informed modification of the ResNet-18 architecture for identifying occlusion myocardial infarction (OMI) -- a severe, often ST-elevation-negative heart attack caused by complete blockage of a coronary artery -- from a single 12-lead ECG. The network first learns lead-specific temporal features via 1xk temporal convolutions, then learns cross-lead spatial concordance/discordance (e.g. reciprocal ST changes) via a 12x1 spatial convolution placed after the residual blocks, with saliency maps highlighting the most relevant leads and waveform regions for explainability. Benchmarked against ResNet-18 and other CNN/random-forest baselines on a multisite real-world clinical dataset of 10,893 ECGs (OMI rate 6.5%), reaching a test AUROC of 0.889 and an average precision of 0.587, outperforming the compared models.