Work

Crafting Deep Learning Models for Classifying ECG Paper Printouts

Year: 2024

Type: conference-paper

Abstract: 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.

Source: Computing in cardiology

DOI: 10.22489/cinc.2024.210

Authors: Damian Marek Kucharski, Arkadiusz Paweł Czerwiński, Agata Maria Wijata, Jacek Kawa, Yalin Zheng, Gregory Lip, Jakub Nalepa

Institutions: Silesian University of Technology, University of Liverpool, Liverpool Heart and Chest Hospital, Liverpool John Moores University, Aalborg University

Language: English

Topic: ECG Monitoring and Analysis

Subfield: Cardiology and Cardiovascular Medicine

Field: Medicine

Domain: Health Sciences

Open Access status: bronze

Funders: Infrastruktura PL-Grid, Academic Computer Centre Cyfronet, AGH University of Science and Technology

OpenAlex ID: W4405434880

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