Graphical analysis of the progression of atrial arrhythmia through an ensemble of Generative Adversarial Network Discriminators
Authors
Nahuel Costa, Jesus Fernandez, Inés Couso, Luciano Sanchez
Corresponding Author
Luciano Sanchez
Available Online August 2019.
- 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/).
Cite this article
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 -