Crafting Deep Learning Models for Classifying ECG Paper Printouts

Damian Kucharski; Arkadiusz Paweł Czerwiński; Agata M. Wijata; Jacek Kawa; Yalin Zheng; Professor Gregory Lip; Jakub Nalepa

doi:10.22489/cinc.2024.210

Abstract

1 min read

As part of the George B. Moody PhysioNet Challenge 2024, we (the GIRAFFE team) built an approach based on InceptionV3 to classify the electrocardiogram (ECG) images.To deal with the class imbalance, we use the Generalized Extreme Value activation function and loss weighting.For the classification task, our best model received a macro F -measure of 0.652 over the hidden test data.Because we had not submitted any unofficial phase entry, we were not included in the official rankings.

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