No abstract

Objective: A classifier based on weighted voting of multiple single-lead based models combining deep learning representation and hand-crafted features was developed to classify 26 cardiac abnormalities from different lead subsets of short-term electrocardiograms (ECG). Approach: A two-stage method was proposed for the multilead prediction. First a lead-agnostic hybrid classifier was trained to predict the pathologies from single-lead ECG signals. The classifier combined fully automated deep learning (DL) features extracted through a convolutional neural network with hand-crafted features through a fully connected layer. Second, a voting of the single-lead based predictions was performed. For the 12-lead subset, voting consisted in an optimised weighting of the output probabilities of all available single lead predictions. For other lead subsets, voting simply consisted in the average of the lead predictions. Main Results: This approach achieved a challenge test score of 0.48, 0.47, 0.46, 0.46, 0.45 on the 12, 6, 4, 3, 2-lead subsets respectively on the 2021 Physionet/Computing in Cardiology challenge hidden test set. The use of an hybrid approach and more advanced voting layer improved some individual class classification but did not offer better generalization than our baseline fully DL approach. Significance: The proposed approach showed potential at correctly classifying main cardiac abnormalities and dealt well with reduced lead subsets.