Figure 2.

Diagram depicting the deep learning model architecture. The deep learning model used a combination of CNN and LSTM to classify STEMI on 12-lead ECGs. The architecture of the proposed AI mode was composed of two 1D -CNNs blocks fed with chest and limb leads, to extract the features from the 6-lead signals. The outputs of the two 1D-CNNs were connected to two layers of LSTM, which served as a sequence analyzer. Then the outputs of the two LSTMs were concatenated and connected to a fully connected layer to classify the data as “STEMI” or “Not STEMI”.