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Modeling and Estimation of Temporal Episode Patterns in Paroxysmal Atrial Fibrillation

Henriksson, Mikael LU ; Martin-Yebra, Alba LU ; Butkuviene, Monika ; Rasmussen, Jakob Gulddahl ; Marozas, Vaidotas ; Petrenas, Andrius ; Savelev, Aleksei ; Platonov, Pyotr G. LU and Sornmo, Leif LU (2021) In IEEE Transactions on Biomedical Engineering 68(1). p.319-329
Abstract

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... (More)

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.

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author
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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
alternating bivariate Hawkes model, Atrial fibrillation, episode clustering, maximum likelihood estimation, point process modeling
in
IEEE Transactions on Biomedical Engineering
volume
68
issue
1
article number
9097442
pages
11 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • pmid:32746005
  • scopus:85098554169
ISSN
0018-9294
DOI
10.1109/TBME.2020.2995563
language
English
LU publication?
yes
id
9f8dc117-df16-491a-945e-657f3f364024
date added to LUP
2021-01-14 08:39:57
date last changed
2024-04-17 23:56:06
@article{9f8dc117-df16-491a-945e-657f3f364024,
  abstract     = {{<p>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.</p>}},
  author       = {{Henriksson, Mikael and Martin-Yebra, Alba and Butkuviene, Monika and Rasmussen, Jakob Gulddahl and Marozas, Vaidotas and Petrenas, Andrius and Savelev, Aleksei and Platonov, Pyotr G. and Sornmo, Leif}},
  issn         = {{0018-9294}},
  keywords     = {{alternating bivariate Hawkes model; Atrial fibrillation; episode clustering; maximum likelihood estimation; point process modeling}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{319--329}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{IEEE Transactions on Biomedical Engineering}},
  title        = {{Modeling and Estimation of Temporal Episode Patterns in Paroxysmal Atrial Fibrillation}},
  url          = {{http://dx.doi.org/10.1109/TBME.2020.2995563}},
  doi          = {{10.1109/TBME.2020.2995563}},
  volume       = {{68}},
  year         = {{2021}},
}