Publication detail

Segmentation of Atrial Electrical Activity in Intracardiac Electrograms (IECGs) Using Convolutional Neural Network (CNN) Trained on Small Imbalanced Dataset

HEJC, J. POSPISIL, D. NOVOTNA, P. PESL, M. JANOUSEK, O. RONZHINA, M. STAREK, Z.

Original Title

Segmentation of Atrial Electrical Activity in Intracardiac Electrograms (IECGs) Using Convolutional Neural Network (CNN) Trained on Small Imbalanced Dataset

Type

conference paper

Language

English

Original Abstract

Timing pattern of intracardiac atrial activity recorded by multipolar catheter in the coronary sinus (CS) provides insightful information about the type and approximate origin of common non-complex arrhythmias. Depending on the anatomy of the CS, the atrial activity can be substantially disturbed by ventricular far field complex preventing accurate segmentation by convential methods. In this paper, we present small clinically validated database of 326 surface 12-lead and intracardiac electrograms (ECG and IEGs) and a simple deep learning framework for semantic beat-to-beat segmentation of atrial activity in CS recordings. The model is based on a residual convolutional neural network (CNN) combined with pyramidal upsampling decoder. It is capable to recognize well between atrial and ventricular signals recorded by decapolar CS catheter in multiple arrhythmic scenarios reaching dice score of 0.875 on evaluation dataset. To address a dataset size and imbalance issues, we have adopted several preprocessing and learning techniques with adequate evaluation of its impact on the model performance.

Keywords

Cardiology; Complex networks; Convolution; Convolutional neural networks; Deep learning; Petroleum reservoir evaluation; Semantics

Authors

HEJC, J.; POSPISIL, D.; NOVOTNA, P.; PESL, M.; JANOUSEK, O.; RONZHINA, M.; STAREK, Z.

Released

1. 10. 2021

Publisher

IEEE

Location

NEW YORK

ISBN

9781665479165

Book

Computing in Cardiology

Edition

September 2021

ISBN

2325-8861

Periodical

Compuing in Cardiology 2013

Year of study

September

Number

1

State

Kingdom of Spain

Pages from

1

Pages to

4

Pages count

4

URL

https://www.cinc.org/archives/2021/pdf/CinC2021-233.pdf

BibTex

@inproceedings{BUT182416,
  author="HEJC, J. and POSPISIL, D. and NOVOTNA, P. and PESL, M. and JANOUSEK, O. and RONZHINA, M. and STAREK, Z.",
  title="Segmentation of Atrial Electrical Activity in Intracardiac Electrograms (IECGs) Using Convolutional Neural Network (CNN) Trained on Small Imbalanced Dataset",
  booktitle="Computing in Cardiology",
  year="2021",
  series="September 2021",
  journal="Compuing in Cardiology 2013",
  volume="September",
  number="1",
  pages="1--4",
  publisher="IEEE",
  address="NEW YORK",
  doi="10.22489/CinC.2021.233",
  isbn="9781665479165",
  issn="2325-8861",
  url="https://www.cinc.org/archives/2021/pdf/CinC2021-233.pdf"
}