Automatic ECG classification and label quality in training data.

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.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.

Feature Selection Based on a Sparse Neural-Network Layer With Normalizing Constraints.

Feature selection (FS) is an important step in machine learning since it has been shown to improve prediction accuracy while suppressing the curse of dimensionality of high-dimensional data. Neural networks have experienced tremendous success in solving many nonlinear learning problems. Here, we propose a new neural-network-based FS approach that introduces two constraints, the satisfaction of which leads to a sparse FS layer.