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Objective: Within the PhysioNet/Computing in Cardiology Challenge 2021, we focused on the design of a machine learning algorithm to identify cardiac abnormalities from electrocardiogram recordings (ECGs) with a various number of leads and to assess the diagnostic potential of reduced-lead ECGs compared to standard 12-lead ECGs. Approach: In our solution, we developed a model based on a deep convolutional neural network, which is a 1D variant of the popular ResNet50 network. This base model was pre-trained on a large training set with our proposed mapping of original labels to SNOMED codes, using three-valued labels. In the next phase, the model was fine-tuned for the Challenge metric and conditions. Main results: In the Challenge, our proposed approach (team CeZIS) achieved a Challenge test score of 0.52 for all lead configurations, placing us 5th out of 39 in the official ranking. Our improved post-Challenge solution was evaluated as the best for all ranked configurations, i.e., for 12-lead, 3-lead, and 2-lead versions of the full test set with the Challenge test score of 0.62, 0.61, and 0.59, respectively. Significance: In addition to building the model for identifying cardiac anomalies, we provide a more detailed description of the issues associated with label mapping and propose its modification in order to obtain a better starting point for training more powerful classification models. We compare the performance of models for different numbers of leads and identify labels for which two leads are sufficient. Moreover, we evaluate the label quality in individual parts of the Challenge training set.

As part of the George B. Moody PhysioNet Challenge 2022, we developed a computational approach to identify abnormal cardiac function from phonocardiograms that combines deep learning and traditional machine learning methods. We adopted a supervised contrastive learning and a deep convolutional neural network to obtain an embedding of the phonocardiogram slice onto a unit hypersphere in low-dimensional space. Thus, we applied the obtained latent factors to classify patients using a Random Forest model. The murmur detection classifier created by our team CeZIS received a weighted accuracy score of 0.756 (ranked 8th out of 40 teams) and Challenge cost score of 11916 (ranked 4th out of 39 teams) on the hidden test set.

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