Publication detail

Deep-Learning Premature Contraction Localization in 12-lead ECG From Whole Signal Annotations

NOVOTNÁ, P. VIČAR, T. RONZHINA, M. HEJČ, J. KOLÁŘOVÁ, J.

Original Title

Deep-Learning Premature Contraction Localization in 12-lead ECG From Whole Signal Annotations

Type

conference paper

Language

English

Original Abstract

Since common electrocardiography (ECG) diagnostics approaches are time-consuming and arrhythmia-type sensitive, deep-learning methods are state-of-the-art for their detection accuracy. However, premature ventricular contractions' (PVC) localization via common deep-learning approaches requires large training set, therefore Multiple Instance Learning (MIL) framework was applied, where model is trained from whole-signal annotations. Proposed MIL framework is based on 1D Convolutional Neural Network (CNN), with global max-pooling in the last layer. The detection of PVCs' positions was done by the peak detector with specified parameters - threshold, minimal distance and peak prominence. Our method was tested on database containing 1590 ECGs, including 672 signals with PVCs. Dice coefficient reaches 0.947. This simple deep-learning method for the localization of PVC achieves a promising performance while being trainable from the whole-signal annotations instead of positional labels.

Keywords

ECG, electrocardiogram, arrhythmia, localization, global, annotation, PVC, premature ventricular contractions

Authors

NOVOTNÁ, P.; VIČAR, T.; RONZHINA, M.; HEJČ, J.; KOLÁŘOVÁ, J.

Released

30. 9. 2020

Publisher

IEEE

Location

NEW YORK

ISBN

978-1-7281-7382-5

Book

Computing in Cardiology 2020

Edition

47

Edition number

1

ISBN

2325-8861

Periodical

Compuing in Cardiology 2013

State

Kingdom of Spain

Pages from

1

Pages to

4

Pages count

4

URL

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9344059

BibTex

@inproceedings{BUT165491,
  author="Petra {Novotná} and Tomáš {Vičar} and Marina {Filipenská} and Jakub {Hejč} and Jana {Kolářová}",
  title="Deep-Learning Premature Contraction Localization in 12-lead ECG From Whole Signal Annotations",
  booktitle="Computing in Cardiology 2020",
  year="2020",
  series="47",
  journal="Compuing in Cardiology 2013",
  number="1",
  pages="1--4",
  publisher="IEEE",
  address="NEW YORK",
  doi="10.22489/CinC.2020.193",
  isbn="978-1-7281-7382-5",
  issn="2325-8861",
  url="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9344059"
}