Detail publikace

Multiple Instance Learning Framework Used For ECG Premature Contraction Localization

NOVOTNÁ, P.

Originální název

Multiple Instance Learning Framework Used For ECG Premature Contraction Localization

Typ

článek ve sborníku mimo WoS a Scopus

Jazyk

angličtina

Originální abstrakt

We propose the model combining convolutional neural network with multiple instance learning in order to localize the premature atrial contraction and premature ventricular contraction. The model is based on ResNet architecture modified for 1D signal processing. Model was trained on China Physiological Signal Challenge 2018 database extended by manually labeled ground truth positions of premature complexes. The presented method did not reach satisfying results in PAC localization (with dice = 0.127 for avg-pooling implementation). On the other hand, results of lo- calization of PVCs were comparable with other published studies (with dice = 0.952 for avg-pooling implementation).

Klíčová slova

EEICT, ECG, PAC, PVC, CNN, MIL, arrhytmia, localization

Autoři

NOVOTNÁ, P.

Vydáno

3. 5. 2021

Nakladatel

Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií

Místo

Brno

ISBN

978-80-214-5942-7

Kniha

Proceedings I of the 27th Conference STUDENT EEICT 2021

Edice

1

Číslo edice

1

Strany od

311

Strany do

315

Strany počet

5

URL

https://www.fekt.vut.cz/conf/EEICT/archiv/sborniky/EEICT_2021_sbornik_1.pdf

BibTex

@inproceedings{BUT172365,
  author="Petra {Novotná}",
  title="Multiple Instance Learning Framework Used For ECG Premature Contraction Localization",
  booktitle="Proceedings I of the 27th Conference STUDENT EEICT 2021
",
  year="2021",
  series="1",
  number="1",
  pages="311--315",
  publisher="Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií",
  address="Brno",
  isbn="978-80-214-5942-7",
  url="https://www.fekt.vut.cz/conf/EEICT/archiv/sborniky/EEICT_2021_sbornik_1.pdf"
}