Characterization of Atrial Fibrillation Episode Patterns : A Comparative Study
(2024) In IEEE Transactions on Biomedical Engineering 71(1). p.106-113- Abstract
<italic>Objective:</italic> The episode patterns of paroxysmal atrial fibrillation (AF) may carry important information on disease progression and complication risk. However, existing studies offer very little insight into to what extent a quantitative characterization of AF patterns can be trusted given the errors in AF detection and various types of shutdown, i.e., poor signal quality and non-wear. This study explores the performance of AF pattern characterizing parameters in the presence of such errors. <italic>Methods:</italic> To evaluate the performance of the parameters AF aggregation and AF density, both previously proposed to characterize AF patterns, the two measures mean normalized difference and the... (More)
<italic>Objective:</italic> The episode patterns of paroxysmal atrial fibrillation (AF) may carry important information on disease progression and complication risk. However, existing studies offer very little insight into to what extent a quantitative characterization of AF patterns can be trusted given the errors in AF detection and various types of shutdown, i.e., poor signal quality and non-wear. This study explores the performance of AF pattern characterizing parameters in the presence of such errors. <italic>Methods:</italic> To evaluate the performance of the parameters AF aggregation and AF density, both previously proposed to characterize AF patterns, the two measures mean normalized difference and the intraclass correlation coefficient are used to describe agreement and reliability, respectively. The parameters are studied on two PhysioNet databases with annotated AF episodes, also accounting for shutdowns due to poor signal quality. <italic>Results:</italic> The agreement is similar for both parameters when computed for detector-based and annotated patterns, which is 0.80 for AF aggregation and 0.85 for AF density. On the other hand, the reliability differs substantially, with 0.96 for AF aggregation but only 0.29 for AF density. This finding suggests that AF aggregation is considerably less sensitive to detection errors. The results from comparing three strategies to handle shutdowns vary considerably, with the strategy that disregards the shutdown from the annotated pattern showing the best agreement and reliability. <italic>Conclusions:</italic> Due to its better robustness to detection errors, AF aggregation should be preferred. To further improve performance, future research should put more emphasis on AF pattern characterization.
(Less)
- author
- Butkuviene, Monika ; Petrenas, Andrius ; Martin-Yebra, Alba ; Marozas, Vaidotas and Sornmo, Leif LU
- organization
- publishing date
- 2024
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- agreement, Atrial fibrillation, Biological system modeling, Biomedical engineering, Databases, detection, Detectors, Electrocardiography, Paroxysmal atrial fibrillation, performance evaluation, Recording, reliability
- in
- IEEE Transactions on Biomedical Engineering
- volume
- 71
- issue
- 1
- pages
- 8 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- pmid:37418404
- scopus:85164417205
- ISSN
- 0018-9294
- DOI
- 10.1109/TBME.2023.3293252
- language
- English
- LU publication?
- yes
- id
- a2a466e9-035d-4f4b-a513-3d7baf055642
- date added to LUP
- 2023-10-16 14:43:02
- date last changed
- 2024-04-19 02:25:07
@article{a2a466e9-035d-4f4b-a513-3d7baf055642,
abstract = {{<p><italic>Objective:</italic> The episode patterns of paroxysmal atrial fibrillation (AF) may carry important information on disease progression and complication risk. However, existing studies offer very little insight into to what extent a quantitative characterization of AF patterns can be trusted given the errors in AF detection and various types of shutdown, i.e., poor signal quality and non-wear. This study explores the performance of AF pattern characterizing parameters in the presence of such errors. <italic>Methods:</italic> To evaluate the performance of the parameters AF aggregation and AF density, both previously proposed to characterize AF patterns, the two measures mean normalized difference and the intraclass correlation coefficient are used to describe agreement and reliability, respectively. The parameters are studied on two PhysioNet databases with annotated AF episodes, also accounting for shutdowns due to poor signal quality. <italic>Results:</italic> The agreement is similar for both parameters when computed for detector-based and annotated patterns, which is 0.80 for AF aggregation and 0.85 for AF density. On the other hand, the reliability differs substantially, with 0.96 for AF aggregation but only 0.29 for AF density. This finding suggests that AF aggregation is considerably less sensitive to detection errors. The results from comparing three strategies to handle shutdowns vary considerably, with the strategy that disregards the shutdown from the annotated pattern showing the best agreement and reliability. <italic>Conclusions:</italic> Due to its better robustness to detection errors, AF aggregation should be preferred. To further improve performance, future research should put more emphasis on AF pattern characterization.</p>}},
author = {{Butkuviene, Monika and Petrenas, Andrius and Martin-Yebra, Alba and Marozas, Vaidotas and Sornmo, Leif}},
issn = {{0018-9294}},
keywords = {{agreement; Atrial fibrillation; Biological system modeling; Biomedical engineering; Databases; detection; Detectors; Electrocardiography; Paroxysmal atrial fibrillation; performance evaluation; Recording; reliability}},
language = {{eng}},
number = {{1}},
pages = {{106--113}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{IEEE Transactions on Biomedical Engineering}},
title = {{Characterization of Atrial Fibrillation Episode Patterns : A Comparative Study}},
url = {{http://dx.doi.org/10.1109/TBME.2023.3293252}},
doi = {{10.1109/TBME.2023.3293252}},
volume = {{71}},
year = {{2024}},
}