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Breathing pattern characterization in chronic heart failure patients using the respiratory flow signal

Garde, A.; Sörnmo, Leif LU ; Jane, R. and Giraldo, B. F. (2010) In Annals of Biomedical Engineering 38(12). p.3572-3580
Abstract
This study proposes a method for the characterization of respiratory patterns in chronic heart failure (CHF) patients with periodic breathing (PB) and nonperiodic breathing (nPB), using the flow signal. Autoregressive modeling of the envelope of the respiratory flow signal is the starting point for the pattern characterization. Spectral parameters extracted from the discriminant frequency band (DB) are used to characterize the respiratory patterns. For each classification problem, the most discriminant parameter subset is selected using the leave-one-out cross-validation technique. The power in the right DB provides an accuracy of 84.6% when classifying PB vs. nPB patterns in CHF patients, whereas the power of the DB provides an accuracy... (More)
This study proposes a method for the characterization of respiratory patterns in chronic heart failure (CHF) patients with periodic breathing (PB) and nonperiodic breathing (nPB), using the flow signal. Autoregressive modeling of the envelope of the respiratory flow signal is the starting point for the pattern characterization. Spectral parameters extracted from the discriminant frequency band (DB) are used to characterize the respiratory patterns. For each classification problem, the most discriminant parameter subset is selected using the leave-one-out cross-validation technique. The power in the right DB provides an accuracy of 84.6% when classifying PB vs. nPB patterns in CHF patients, whereas the power of the DB provides an accuracy of 85.5% when classifying the whole group of CHF patients vs. healthy subjects, and 85.2% when classifying nPB patients vs. healthy subjects. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Respiratory pattern, Discriminant, Periodic and nonperiodic breathing, band, Chronic heart failure, AR modeling
in
Annals of Biomedical Engineering
volume
38
issue
12
pages
3572 - 3580
publisher
Springer
external identifiers
  • wos:000284062200003
  • scopus:78651396520
ISSN
1573-9686
DOI
10.1007/s10439-010-0109-0
language
English
LU publication?
yes
id
9ced5ec5-5e36-48d1-bdb5-87186929fc1b (old id 1752687)
date added to LUP
2010-12-30 07:50:13
date last changed
2018-05-29 10:56:17
@article{9ced5ec5-5e36-48d1-bdb5-87186929fc1b,
  abstract     = {This study proposes a method for the characterization of respiratory patterns in chronic heart failure (CHF) patients with periodic breathing (PB) and nonperiodic breathing (nPB), using the flow signal. Autoregressive modeling of the envelope of the respiratory flow signal is the starting point for the pattern characterization. Spectral parameters extracted from the discriminant frequency band (DB) are used to characterize the respiratory patterns. For each classification problem, the most discriminant parameter subset is selected using the leave-one-out cross-validation technique. The power in the right DB provides an accuracy of 84.6% when classifying PB vs. nPB patterns in CHF patients, whereas the power of the DB provides an accuracy of 85.5% when classifying the whole group of CHF patients vs. healthy subjects, and 85.2% when classifying nPB patients vs. healthy subjects.},
  author       = {Garde, A. and Sörnmo, Leif and Jane, R. and Giraldo, B. F.},
  issn         = {1573-9686},
  keyword      = {Respiratory pattern,Discriminant,Periodic and nonperiodic breathing,band,Chronic heart failure,AR modeling},
  language     = {eng},
  number       = {12},
  pages        = {3572--3580},
  publisher    = {Springer},
  series       = {Annals of Biomedical Engineering},
  title        = {Breathing pattern characterization in chronic heart failure patients using the respiratory flow signal},
  url          = {http://dx.doi.org/10.1007/s10439-010-0109-0},
  volume       = {38},
  year         = {2010},
}