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Detection of occult paroxysmal atrial fibrillation

Petrenas, Andrius; Sörnmo, Leif LU ; Lukosevicius, Arunas and Marozas, Vaidotas (2015) In Medical & Biological Engineering & Computing 53(4). p.287-297
Abstract
This work introduces a novel approach to the detection of brief episodes of paroxysmal atrial fibrillation (PAF). The proposed detector is based on four parameters which characterize RR interval irregularity, P-wave absence, f-wave presence, and noise level, of which the latter three are determined from a signal produced by an echo state network. The parameters are used for fuzzy logic classification where the decisions involve information on prevailing signal quality; no training is required. The performance is evaluated on a large set of test signals with brief episodes of PAF. The results show that episodes with as few as five beats can be reliably detected with an accuracy of 0.88, compared to 0.82 for a detector based on rhythm... (More)
This work introduces a novel approach to the detection of brief episodes of paroxysmal atrial fibrillation (PAF). The proposed detector is based on four parameters which characterize RR interval irregularity, P-wave absence, f-wave presence, and noise level, of which the latter three are determined from a signal produced by an echo state network. The parameters are used for fuzzy logic classification where the decisions involve information on prevailing signal quality; no training is required. The performance is evaluated on a large set of test signals with brief episodes of PAF. The results show that episodes with as few as five beats can be reliably detected with an accuracy of 0.88, compared to 0.82 for a detector based on rhythm information only (the coefficient of sample entropy); this difference in accuracy increases when atrial premature beats are present. The results also show that the performance remains essentially unchanged at noise levels up to RMS. It is concluded that the combination of information on ventricular activity, atrial activity, and noise leads to substantial improvement when detecting brief episodes of PAF. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Paroxysmal atrial fibrillation, Brief episodes, Detection, Echo state, network, Fuzzy logic
in
Medical & Biological Engineering & Computing
volume
53
issue
4
pages
287 - 297
publisher
Springer
external identifiers
  • wos:000351291500001
  • scopus:84925541802
ISSN
0140-0118
DOI
10.1007/s11517-014-1234-y
language
English
LU publication?
yes
id
e6ca93ae-6515-432e-bb94-3ddbc264cdf3 (old id 5294039)
date added to LUP
2015-04-24 15:19:10
date last changed
2017-07-23 04:21:54
@article{e6ca93ae-6515-432e-bb94-3ddbc264cdf3,
  abstract     = {This work introduces a novel approach to the detection of brief episodes of paroxysmal atrial fibrillation (PAF). The proposed detector is based on four parameters which characterize RR interval irregularity, P-wave absence, f-wave presence, and noise level, of which the latter three are determined from a signal produced by an echo state network. The parameters are used for fuzzy logic classification where the decisions involve information on prevailing signal quality; no training is required. The performance is evaluated on a large set of test signals with brief episodes of PAF. The results show that episodes with as few as five beats can be reliably detected with an accuracy of 0.88, compared to 0.82 for a detector based on rhythm information only (the coefficient of sample entropy); this difference in accuracy increases when atrial premature beats are present. The results also show that the performance remains essentially unchanged at noise levels up to RMS. It is concluded that the combination of information on ventricular activity, atrial activity, and noise leads to substantial improvement when detecting brief episodes of PAF.},
  author       = {Petrenas, Andrius and Sörnmo, Leif and Lukosevicius, Arunas and Marozas, Vaidotas},
  issn         = {0140-0118},
  keyword      = {Paroxysmal atrial fibrillation,Brief episodes,Detection,Echo state,network,Fuzzy logic},
  language     = {eng},
  number       = {4},
  pages        = {287--297},
  publisher    = {Springer},
  series       = {Medical & Biological Engineering & Computing},
  title        = {Detection of occult paroxysmal atrial fibrillation},
  url          = {http://dx.doi.org/10.1007/s11517-014-1234-y},
  volume       = {53},
  year         = {2015},
}