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Atrial Fibrillation Episode Patterns and Their Influence on Detection Performance

Butkuviene, Monika ; Petrenas, Andrius ; Solosenko, Andrius ; Martin-Yebra, Alba ; Marozas, Vaidotas and Sornmo, Leif LU (2021) 2021 Computing in Cardiology, CinC 2021 In Computing in Cardiology 2021-September.
Abstract

Existing studies offer little insight on how atrial fibrillation (AF) detection performance is influenced by the properties of AF episode patterns. The aim of this study is to investigate the influence of AF burden and median AF episode length on detection performance. For this purpose, three types of AF detectors, using either information on rhythm, rhythm and morphology, or ECG segments, were investigated on 1-h simulated ECGs. Comparing AF burdens of 20% and 80% for a median episode length of 167 beats, the sensitivity of the rhythm- and morphology-based detector increases only slightly whereas the specificity drops from 99.5% to 93.3%. The corresponding figures of specificity are 99.0% and 90.6% for the rhythm-based detector; 88.1%... (More)

Existing studies offer little insight on how atrial fibrillation (AF) detection performance is influenced by the properties of AF episode patterns. The aim of this study is to investigate the influence of AF burden and median AF episode length on detection performance. For this purpose, three types of AF detectors, using either information on rhythm, rhythm and morphology, or ECG segments, were investigated on 1-h simulated ECGs. Comparing AF burdens of 20% and 80% for a median episode length of 167 beats, the sensitivity of the rhythm- and morphology-based detector increases only slightly whereas the specificity drops from 99.5% to 93.3%. The corresponding figures of specificity are 99.0% and 90.6% for the rhythm-based detector; 88.1% and 70.7% for the segment-based detector. The influence of AF burden on specificity becomes even more pronounced for AF patterns with brief episodes (median episode length set to 30 beats). Therefore, patterns with briefepisodes and high AF burden imply higher demands on detection performance. Future research should focus on how well episode patterns are captured.

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author
; ; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
2021 Computing in Cardiology, CinC 2021
series title
Computing in Cardiology
volume
2021-September
publisher
IEEE Computer Society
conference name
2021 Computing in Cardiology, CinC 2021
conference location
Brno, Czech Republic
conference dates
2021-09-13 - 2021-09-15
external identifiers
  • scopus:85124744440
ISSN
2325-887X
2325-8861
ISBN
9781665479165
DOI
10.23919/CinC53138.2021.9662847
language
English
LU publication?
yes
id
d45d3f6e-7d8a-4183-8c49-cf49cabb4d3f
date added to LUP
2022-04-12 09:12:05
date last changed
2022-08-04 23:39:15
@inproceedings{d45d3f6e-7d8a-4183-8c49-cf49cabb4d3f,
  abstract     = {{<p>Existing studies offer little insight on how atrial fibrillation (AF) detection performance is influenced by the properties of AF episode patterns. The aim of this study is to investigate the influence of AF burden and median AF episode length on detection performance. For this purpose, three types of AF detectors, using either information on rhythm, rhythm and morphology, or ECG segments, were investigated on 1-h simulated ECGs. Comparing AF burdens of 20% and 80% for a median episode length of 167 beats, the sensitivity of the rhythm- and morphology-based detector increases only slightly whereas the specificity drops from 99.5% to 93.3%. The corresponding figures of specificity are 99.0% and 90.6% for the rhythm-based detector; 88.1% and 70.7% for the segment-based detector. The influence of AF burden on specificity becomes even more pronounced for AF patterns with brief episodes (median episode length set to 30 beats). Therefore, patterns with briefepisodes and high AF burden imply higher demands on detection performance. Future research should focus on how well episode patterns are captured. </p>}},
  author       = {{Butkuviene, Monika and Petrenas, Andrius and Solosenko, Andrius and Martin-Yebra, Alba and Marozas, Vaidotas and Sornmo, Leif}},
  booktitle    = {{2021 Computing in Cardiology, CinC 2021}},
  isbn         = {{9781665479165}},
  issn         = {{2325-887X}},
  language     = {{eng}},
  publisher    = {{IEEE Computer Society}},
  series       = {{Computing in Cardiology}},
  title        = {{Atrial Fibrillation Episode Patterns and Their Influence on Detection Performance}},
  url          = {{http://dx.doi.org/10.23919/CinC53138.2021.9662847}},
  doi          = {{10.23919/CinC53138.2021.9662847}},
  volume       = {{2021-September}},
  year         = {{2021}},
}