Atrial Fibrillation Episode Patterns and Their Influence on Detection Performance
(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
- Butkuviene, Monika ; Petrenas, Andrius ; Solosenko, Andrius ; Martin-Yebra, Alba ; Marozas, Vaidotas and Sornmo, Leif LU
- organization
- publishing date
- 2021
- 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
- 2024-08-26 06:33:07
@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}}, }