Classification of ECG using ensemble of residual CNNs with or without attention mechanism

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F22%3A00557480" target="_blank" >RIV/68081731:_____/22:00557480 - isvavai.cz</a>

Result on the web

<a href="https://iopscience.iop.org/article/10.1088/1361-6579/ac647c" target="_blank" >https://iopscience.iop.org/article/10.1088/1361-6579/ac647c</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1088/1361-6579/ac647c" target="_blank" >10.1088/1361-6579/ac647c</a>

Result language

angličtina

Original language name

Classification of ECG using ensemble of residual CNNs with or without attention mechanism

Original language description

Objective. This paper introduces a winning solution (team ISIBrno-AIMT) to the official round of PhysioNet Challenge 2021. The main goal of the challenge was a classification of ECG recordings into 26 multi-label pathological classes with a variable number of leads (e.g. 12, 6, 4, 3, 2). The main objective of this study is to verify whether the multi-head-attention mechanism influences the model performance. Approach. We introduced an ECG classification method based on the ResNet architecture with a multi-head attention mechanism for the official round of the challenge. However, empirical findings collected during model development suggested that the multi-head attention layer might not significantly impact the final classification performance. For this reason, during the follow-up round, we removed a multi-head attention layer to test the influence on model performance. Like the official round, the model is optimized using a mixture of loss functions, i.e. binary cross-entropy, custom challenge score loss function, and custom sparsity loss function. Probability thresholds for each classification class are estimated using the evolutionary optimization method. The final architecture consists of three submodels forming a majority voting classification ensemble. Main results. The modified model without the multi-head attention layer increased the overall challenge score to 0.59 compared to the 0.58 from the official round. Significance. Our findings from the follow-up submission support the fact that the multi-head attention layer in the proposed architecture does not significantly affect the classification performance.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20601 - Medical engineering

Project

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

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

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

Name of the periodical

Physiological Measurement

ISSN

0967-3334

e-ISSN

1361-6579

Volume of the periodical

43

Issue of the periodical within the volume

4

Country of publishing house

GB - UNITED KINGDOM

Number of pages

12

Pages from-to

044001

UT code for WoS article

000790542900001

EID of the result in the Scopus database

2-s2.0-85129997154