Hidden Markov Models for Activity Detection in Atrial Fibrillation Electrograms

Ríos Muñoz, Gonzalo Ricardo; Moreno Pino, Fernando; Soto, Nina; Martínez Olmos, Pablo; Artés Rodríguez, Antonio; Fernández Avilés, Francisco; Arenal, Ángel

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Hidden Markov Models for Activity Detection in Atrial Fibrillation Electrograms

Author(s): Ríos Muñoz, Gonzalo Ricardo; Moreno Pino, Fernando; Soto, Nina; Martínez Olmos, Pablo; Artés Rodríguez, Antonio; Fernández Avilés, Francisco; Arenal, Ángel

Publisher: IEEE

Issued date: 2020-09-13

Citation: Computing in Cardiology (CinC 2020), 13-16 September 2020, Rimini, Italy. V. 47. IEEE, 2020.

ISBN: 978-1-7281-7382-5

ISSN: 2325-887X

DOI: https://doi.org/10.22489/CinC.2020.098

xmlui.dri2xhtml.METS-1.0.item-contributor-funder: Comunidad de Madrid
Ministerio de Ciencia, Innovación y Universidades (España)

Sponsor: This study was supported by grants PI18/01895 from the Instituto de Salud Carlos III, and RD16/0011/0029 Red de Terapia Celular from the Instituto de Salud Carlos III, the projects RTI2018-099655-B-I00; TEC2017-92552-EXP; PID2019-108539RB-C22, Y2018/TCS-4705, and the support of NVIDIA Corporation with the donation of the Titan V GPU used during this research.

Project: Gobierno de España. RTI2018-099655-B-I00
Gobierno de España. TEC2017-92552-EXP
Gobierno de España. PID2019-108539RB-C22
Comunidad de Madrid. Y2018/TCS-4705

Keywords: Training , Low voltage , Nonlinear distortion , Hidden Markov models , Atrial fibrillation , Training data , Robustness

URI: http://hdl.handle.net/10016/34837

Abstract:

Activity detection in atrial fibrillation (AF) electrograms (EGMs) is a key concept to understand the mechanisms of this frequent arrhythmia and design new strategies for its treatment. We present a new method that employs Hidden Markov Models (HMMs) to identi [+]