Graphical analysis of the progression of atrial arrhythmia through an ensemble of Generative Adversarial Network Discriminators

DOI

10.2991/eusflat-19.2019.78How to use a DOI?

Keywords

Heart Disease Generative Adversarial Networks Time Series

Abstract

Logs of arrhythmia episodes in patients with pacemakers are used to estimate the temporal progression of atrial arrhythmia. In order to attain an early detection, a stream of dates and episode lengths are fed to an array of detectors, each of which is responsive to a narrow range of arrhythmias. The outputs of these detectors are organized on a projection map, used by the specialist to assess the risk in the evolution of the patient. Each of the mentioned detectors is a recurrent LSTM network, that is in turn the discriminating element of a GAN that has been trained to generate temporal sequences of values of the degrees of truth that the arrhythmia episodes are not isolated.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Nahuel Costa
AU  - Jesus Fernandez
AU  - Inés Couso
AU  - Luciano Sanchez
PY  - 2019/08
DA  - 2019/08
TI  - Graphical analysis of the progression of atrial arrhythmia through an ensemble of Generative Adversarial Network Discriminators
BT  - Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019)
PB  - Atlantis Press
SP  - 566
EP  - 572
SN  - 2589-6644
UR  - https://doi.org/10.2991/eusflat-19.2019.78
DO  - 10.2991/eusflat-19.2019.78
ID  - Costa2019/08
ER  -