Utilization of Residual CNN-GRU With Attention Mechanism for Classification of 12-lead ECG
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F20%3A00543689" target="_blank" >RIV/68081731:_____/20:00543689 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9344152" target="_blank" >https://ieeexplore.ieee.org/document/9344152</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.22489/CinC.2020.032" target="_blank" >10.22489/CinC.2020.032</a>
Alternative languages
Result language
angličtina
Original language name
Utilization of Residual CNN-GRU With Attention Mechanism for Classification of 12-lead ECG
Original language description
Cardiac diseases are the most common cause of death. The fully automated classification of the electrocardiogram (ECG) supports early capturing of heart disorders, and, consequently, may help to get treatment early. Here in this paper, we introduce a deep neural network for human ECG classification into 24 independent groups, for example, atrial fibrillation, 1st degree AV block, Bundle branch blocks, premature contractions, changes in the ST segment, normal sinus rhythm, and others. The network architecture utilizes a convolutional neural network with residual blocks, bidirectional Gated Recurrent Units, and an attention mechanism. The algorithm was trained and validated on the public dataset proposed by the PhysioNet Challenge 2020. The trained algorithm was tested using a hidden test set during the official phase of the challenge and obtained the challenge validation score of 0.659 as entries by the ISIBrno team. The final testing scores were 0.847, 0.195,0.006, and 0.122, for testing sets I, II, III, and full test set, respectively. We have obtained 30th place out of 41 teams in the official ranking.
Czech name
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Czech description
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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
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2020 Computing in Cardiology (CinC 2020)
ISBN
978-1-7281-7382-5
ISSN
2325-8861
e-ISSN
—
Number of pages
4
Pages from-to
"Č. 47 (2020)"
Publisher name
IEEE
Place of publication
New York
Event location
Rimini
Event date
Sep 13, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000657257000053