RT Conference Proceedings
T1 Hidden Markov Models for Activity Detection in Atrial Fibrillation Electrograms
A1 Ríos Muñoz, Gonzalo Ricardo
A1 Moreno Pino, Fernando
A1 Soto, Nina
A1 Martínez Olmos, Pablo
A1 Artés Rodríguez, Antonio
A1 Fernández Avilés, Francisco
A1 Arenal, Ángel
AB 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 identify activity presence in bipolar EGMs. The method is fully unsupervised and hence it does not require labeled training data. The HMM activity detection method was validated and compared to the non-linear energy operator (NLEO) method for a set of manually annotated EGMs. The HMM performed better than the NLEO and exhibited more robustness in the presence of low voltage fragmented EGMs.
PB IEEE
SN 978-1-7281-7382-5
SN 2325-887X
YR 2020
FD 2020-09-13
LK https://hdl.handle.net/10016/34837
UL https://hdl.handle.net/10016/34837
LA eng
NO Proceeding of 2020 Computing in Cardiology (CinC 2020), 13-16 September 2020, Rimini, Italy
NO 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.
DS e-Archivo
RD 19 jul. 2024