A Two-Phase Multilabel ECG Classification Using One-Dimensional Convolutional Neural Network and Modified Labels

Within PhysioNet/Computing in Cardiology Challenge 2021, we developed a two-phase method of automatic ECG recording classification. In the first phase, we pre-trained a model on a large training set with our proposed mapping of original labels to the SNOMED codes, using threevalued labels. To solve the multilabel binary classification task, we used a deep convolutional neural network, which is a 1D variant of the popular ResNet50 network. In the second phase, we performed fine-tuning for the Challenge metric and conditions. Our team CeZIS took 6
^th
, 3
^rd
, 5th, 4
^th
, and 5th places for the 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead versions of the hidden test set, respectively, with a score of 0.52 for each lead configuration.