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Low-complexity detection of atrial fibrillation in continuous long-term monitoring.

Petrėnas, Andrius; Marozas, Vaidotas and Sörnmo, Leif LU (2015) In Computers in Biology and Medicine 65(Online 28 January 2015). p.184-191
Abstract
This study describes an atrial fibrillation (AF) detector whose structure is well-adapted both for detection of subclinical AF episodes and for implementation in a battery-powered device for use in continuous long-term monitoring applications. A key aspect for achieving these two properties is the use of an 8-beat sliding window, which thus is much shorter than the 128-beat window used in most existing AF detectors. The building blocks of the proposed detector include ectopic beat filtering, bigeminal suppression, characterization of RR interval irregularity, and signal fusion. With one design parameter, the performance can be tuned to put more emphasis on avoiding false alarms due to non-AF arrhythmias or more emphasis on detecting brief... (More)
This study describes an atrial fibrillation (AF) detector whose structure is well-adapted both for detection of subclinical AF episodes and for implementation in a battery-powered device for use in continuous long-term monitoring applications. A key aspect for achieving these two properties is the use of an 8-beat sliding window, which thus is much shorter than the 128-beat window used in most existing AF detectors. The building blocks of the proposed detector include ectopic beat filtering, bigeminal suppression, characterization of RR interval irregularity, and signal fusion. With one design parameter, the performance can be tuned to put more emphasis on avoiding false alarms due to non-AF arrhythmias or more emphasis on detecting brief AF episodes. Despite its very simple structure, the results show that the detector performs better on the MIT-BIH Atrial Fibrillation database than do existing detectors, with high sensitivity and specificity (97.1% and 98.3%, respectively). The detector can be implemented with just a few arithmetical operations and does not require a large memory buffer thanks to the short window. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Computers in Biology and Medicine
volume
65
issue
Online 28 January 2015
pages
184 - 191
publisher
Elsevier
external identifiers
  • pmid:25666902
  • wos:000362860700022
  • scopus:84942366412
ISSN
1879-0534
DOI
10.1016/j.compbiomed.2015.01.019
language
English
LU publication?
yes
id
886dead0-b11c-46e3-9dc8-50bbaa6b1674 (old id 5144928)
date added to LUP
2015-03-09 14:04:05
date last changed
2017-10-08 03:17:30
@article{886dead0-b11c-46e3-9dc8-50bbaa6b1674,
  abstract     = {This study describes an atrial fibrillation (AF) detector whose structure is well-adapted both for detection of subclinical AF episodes and for implementation in a battery-powered device for use in continuous long-term monitoring applications. A key aspect for achieving these two properties is the use of an 8-beat sliding window, which thus is much shorter than the 128-beat window used in most existing AF detectors. The building blocks of the proposed detector include ectopic beat filtering, bigeminal suppression, characterization of RR interval irregularity, and signal fusion. With one design parameter, the performance can be tuned to put more emphasis on avoiding false alarms due to non-AF arrhythmias or more emphasis on detecting brief AF episodes. Despite its very simple structure, the results show that the detector performs better on the MIT-BIH Atrial Fibrillation database than do existing detectors, with high sensitivity and specificity (97.1% and 98.3%, respectively). The detector can be implemented with just a few arithmetical operations and does not require a large memory buffer thanks to the short window.},
  author       = {Petrėnas, Andrius and Marozas, Vaidotas and Sörnmo, Leif},
  issn         = {1879-0534},
  language     = {eng},
  number       = {Online 28 January 2015},
  pages        = {184--191},
  publisher    = {Elsevier},
  series       = {Computers in Biology and Medicine},
  title        = {Low-complexity detection of atrial fibrillation in continuous long-term monitoring.},
  url          = {http://dx.doi.org/10.1016/j.compbiomed.2015.01.019},
  volume       = {65},
  year         = {2015},
}