Detail publikace

Cardiac Abnormalities Recognition in ECG Using a Convolutional Network with Attention and Input with an Adaptable Number of Leads

VIČAR, T. NOVOTNÁ, P. HEJČ, J. JANOUŠEK, O. RONZHINA, M.

Originální název

Cardiac Abnormalities Recognition in ECG Using a Convolutional Network with Attention and Input with an Adaptable Number of Leads

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

In this work, we present an algorithm for automatically identifying the cardiac abnormalities in ECG records with the various number of leads. The algorithm is based on the modified ResNet convolutional neural network with the attention layer. The network input is modified to allow using a single network for different lead subsets. In an official phase challenge entry, our BUTTeam reached the 15th place. In our test challenge entry, we have achieved 0.470, 0.460, 0.470, 0.460, and 0.460 of the challenge metric for 12, 6, 4, 3 and 2 leads with ranking 14th, 14th, 11th, 15th and 11th place, respectively. From additional evaluation of other lead subsets, the leads representing a common heart axis orientation achieved the best detection results. However, all lead subsets performed very similarly.

Klíčová slova

arrhythmias; cardiac abnormalities; convolutional neural network; attention layer; ResNet

Autoři

VIČAR, T.; NOVOTNÁ, P.; HEJČ, J.; JANOUŠEK, O.; RONZHINA, M.

Vydáno

18. 11. 2021

Nakladatel

Computing in Cardiology 2021

Místo

Brno

ISSN

2325-887X

Periodikum

Computing in Cardiology

Stát

Spojené státy americké

Strany od

1

Strany do

4

Strany počet

4

URL

https://www.cinc.org/archives/2021/pdf/CinC2021-047.pdf

BibTex

@inproceedings{BUT173258,
  author="Tomáš {Vičar} and Petra {Novotná} and Jakub {Hejč} and Oto {Janoušek} and Marina {Filipenská}",
  title="Cardiac Abnormalities Recognition in ECG Using a Convolutional Network with Attention and Input with an Adaptable Number of Leads",
  booktitle="Computing in Cardiology 2021",
  year="2021",
  journal="Computing in Cardiology",
  pages="1--4",
  publisher="Computing in Cardiology 2021",
  address="Brno",
  doi="10.22489/CinC.2021.047",
  issn="2325-887X",
  url="https://www.cinc.org/archives/2021/pdf/CinC2021-047.pdf"
}