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Classification of ECG Using Ensemble of Residual CNNs with Attention Mechanism

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F21%3A00555163" target="_blank" >RIV/68081731:_____/21:00555163 - isvavai.cz</a>

  • Result on the web

    <a href="https://ieeexplore.ieee.org/document/9662723" target="_blank" >https://ieeexplore.ieee.org/document/9662723</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.23919/CinC53138.2021.9662723" target="_blank" >10.23919/CinC53138.2021.9662723</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Classification of ECG Using Ensemble of Residual CNNs with Attention Mechanism

  • Original language description

    This paper introduces a winning solution (team ISIBrno-AIMT) to the PhysioNet Challenge 2021. The method is based on the ResNet deep neural network architecture with a multi-head attention mechanism for ECG classification into 26 independent groups. The model is optimized using a mixture of loss functions, i.e., binary cross-entropy, custom challenge score loss function, and sparsity loss function. Probability thresholds for each classification class are estimated using the evolutionary optimization method. The final model consists of three submodels forming a majority voting classification ensemble. The proposed method classifies ECGs with a variable number of leads, e.g., 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead. The algorithm was validated and tested on the external hidden datasets (CPSC, G12EC, undisclosed set, UMich), achieving a challenge score 0.58 for all tested lead configurations. The total training time was approximately 27 hours, i.e., 9 hours per model. The presented solution was ranked first across all 39 teams in all categories.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    20601 - Medical engineering

Result continuities

  • Project

    <a href="/en/project/FW01010305" target="_blank" >FW01010305: Artificial Intelligence for Autonomous ECG Classification in Online Telemedicine Platform</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2021

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    2021 Computing in Cardiology (CinC)

  • ISBN

    978-166547916-5

  • ISSN

    2325-8861

  • e-ISSN

    2325-887X

  • Number of pages

    4

  • Pages from-to

    14

  • Publisher name

    IEEE

  • Place of publication

    New York

  • Event location

    Brno

  • Event date

    Sep 12, 2021

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article