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
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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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
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