TY - JOUR
T1 - Modeling and Estimation of Temporal Episode Patterns in Paroxysmal Atrial Fibrillation
AU - Henriksson, Mikael
AU - Martin-Yebra, Alba
AU - Butkuviene, Monika
AU - Rasmussen, Jakob Gulddahl
AU - Marozas, Vaidotas
AU - Petrenas, Andrius
AU - Savelev, Aleksei
AU - Platonov, Pyotr G.
AU - Sornmo, Leif
N1 - Funding Information:
Manuscript received February 5, 2020; revised April 30, 2020; accepted May 14, 2020. Date of publication May 20, 2020; date of current version December 21, 2020. This work was supported in part by the Swedish Research Council (2016-03382), in part by the Research Council of Lithuania (S-MIP-17/81), in part by JGR, in part by the Danish Council for Independent Research (DFF7014-00074), and in part by the Villum Foundation (#8721). (Corresponding author: Alba Martín-Yebra.) Mikael Henriksson and Leif Sörnmo are with the Department of Biomedical Engineering and Center for Integrative Electrocardiology, Lund University.
Publisher Copyright:
© 1964-2012 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/1
Y1 - 2021/1
N2 - Objective: The present study proposes a model-based, statistical approach to characterizing episode patterns in paroxysmal atrial fibrillation (AF). Thanks to the rapid advancement of noninvasive monitoring technology, the proposed approach should become increasingly relevant in clinical practice. Methods: History-dependent point process modeling is employed to characterize AF episode patterns, using a novel alternating, bivariate Hawkes self-exciting model. In addition, a modified version of a recently proposed statistical model to simulate AF progression throughout a lifetime is considered, involving non-Markovian rhythm switching and survival functions. For each model, the maximum likelihood estimator is derived and used to find the model parameters from observed data. Results: Using three databases with a total of 59 long-term ECG recordings, the goodness-of-fit analysis demonstrates that the proposed alternating, bivariate Hawkes model fits SR-to-AF transitions in 40 recordings and AF-to-SR transitions in 51; the corresponding numbers for the AF model with non-Markovian rhythm switching are 40 and 11, respectively. Moreover, the results indicate that the model parameters related to AF episode clustering, i.e., aggregation of temporal AF episodes, provide information complementary to the well-known clinical parameter AF burden. Conclusion: Point process modeling provides a detailed characterization of the occurrence pattern of AF episodes that may improve the understanding of arrhythmia progression.
AB - Objective: The present study proposes a model-based, statistical approach to characterizing episode patterns in paroxysmal atrial fibrillation (AF). Thanks to the rapid advancement of noninvasive monitoring technology, the proposed approach should become increasingly relevant in clinical practice. Methods: History-dependent point process modeling is employed to characterize AF episode patterns, using a novel alternating, bivariate Hawkes self-exciting model. In addition, a modified version of a recently proposed statistical model to simulate AF progression throughout a lifetime is considered, involving non-Markovian rhythm switching and survival functions. For each model, the maximum likelihood estimator is derived and used to find the model parameters from observed data. Results: Using three databases with a total of 59 long-term ECG recordings, the goodness-of-fit analysis demonstrates that the proposed alternating, bivariate Hawkes model fits SR-to-AF transitions in 40 recordings and AF-to-SR transitions in 51; the corresponding numbers for the AF model with non-Markovian rhythm switching are 40 and 11, respectively. Moreover, the results indicate that the model parameters related to AF episode clustering, i.e., aggregation of temporal AF episodes, provide information complementary to the well-known clinical parameter AF burden. Conclusion: Point process modeling provides a detailed characterization of the occurrence pattern of AF episodes that may improve the understanding of arrhythmia progression.
KW - alternating bivariate Hawkes model
KW - Atrial fibrillation
KW - episode clustering
KW - maximum likelihood estimation
KW - point process modeling
UR - http://www.scopus.com/inward/record.url?scp=85098554169&partnerID=8YFLogxK
U2 - 10.1109/TBME.2020.2995563
DO - 10.1109/TBME.2020.2995563
M3 - Journal article
C2 - 32746005
AN - SCOPUS:85098554169
SN - 0018-9294
VL - 68
SP - 319
EP - 329
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
IS - 1
M1 - 9097442
ER -