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Correntropy-Based Spectral Characterization of Respiratory Patterns in Patients With Chronic Heart Failure

Garde, Ainara; Sörnmo, Leif LU ; Jane, Raimon and Giraldo, Beatriz F. (2010) In IEEE Transactions on Biomedical Engineering 57(8). p.1964-1972
Abstract
A correntropy-based technique is proposed for the characterization and classification of respiratory flow signals in chronic heart failure (CHF) patients with periodic or nonperiodic breathing (PB or nPB, respectively) and healthy subjects. The correntropy is a recently introduced, generalized correlation measure whose properties lend themselves to the definition of a correntropy-based spectral density (CSD). Using this technique, both respiratory and modulation frequencies can be reliably detected at their original positions in the spectrum without prior demodulation of the flow signal. Single-parameter classification of respiratory patterns is investigated for three different parameters extracted from the respiratory and modulation... (More)
A correntropy-based technique is proposed for the characterization and classification of respiratory flow signals in chronic heart failure (CHF) patients with periodic or nonperiodic breathing (PB or nPB, respectively) and healthy subjects. The correntropy is a recently introduced, generalized correlation measure whose properties lend themselves to the definition of a correntropy-based spectral density (CSD). Using this technique, both respiratory and modulation frequencies can be reliably detected at their original positions in the spectrum without prior demodulation of the flow signal. Single-parameter classification of respiratory patterns is investigated for three different parameters extracted from the respiratory and modulation frequency bands of the CSD, and one parameter defined by the correntropy mean. The results show that the ratio between the powers in the modulation and respiratory frequency bands provides the best result when classifying CHF patients with either PB or nPB, yielding an accuracy of 88.9%. The correntropy mean offers excellent performance when classifying CHF patients versus healthy subjects, yielding an accuracy of 95.2% and discriminating nPB patients from healthy subjects with an accuracy of 94.4%. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
periodic breathing (PB), linear classification, spectral density (CSD), correntropy, Autoregressive (AR) modeling, chronic heart failure (CHF)
in
IEEE Transactions on Biomedical Engineering
volume
57
issue
8
pages
1964 - 1972
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • wos:000282000900016
  • scopus:77954637792
ISSN
0018-9294
DOI
10.1109/TBME.2010.2044176
language
English
LU publication?
yes
id
eeab3daf-fb95-407c-8e79-eb46d604eeeb (old id 1695941)
date added to LUP
2010-10-25 13:00:41
date last changed
2018-06-24 04:27:52
@article{eeab3daf-fb95-407c-8e79-eb46d604eeeb,
  abstract     = {A correntropy-based technique is proposed for the characterization and classification of respiratory flow signals in chronic heart failure (CHF) patients with periodic or nonperiodic breathing (PB or nPB, respectively) and healthy subjects. The correntropy is a recently introduced, generalized correlation measure whose properties lend themselves to the definition of a correntropy-based spectral density (CSD). Using this technique, both respiratory and modulation frequencies can be reliably detected at their original positions in the spectrum without prior demodulation of the flow signal. Single-parameter classification of respiratory patterns is investigated for three different parameters extracted from the respiratory and modulation frequency bands of the CSD, and one parameter defined by the correntropy mean. The results show that the ratio between the powers in the modulation and respiratory frequency bands provides the best result when classifying CHF patients with either PB or nPB, yielding an accuracy of 88.9%. The correntropy mean offers excellent performance when classifying CHF patients versus healthy subjects, yielding an accuracy of 95.2% and discriminating nPB patients from healthy subjects with an accuracy of 94.4%.},
  author       = {Garde, Ainara and Sörnmo, Leif and Jane, Raimon and Giraldo, Beatriz F.},
  issn         = {0018-9294},
  keyword      = {periodic breathing (PB),linear classification,spectral density (CSD),correntropy,Autoregressive (AR) modeling,chronic heart failure (CHF)},
  language     = {eng},
  number       = {8},
  pages        = {1964--1972},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  series       = {IEEE Transactions on Biomedical Engineering},
  title        = {Correntropy-Based Spectral Characterization of Respiratory Patterns in Patients With Chronic Heart Failure},
  url          = {http://dx.doi.org/10.1109/TBME.2010.2044176},
  volume       = {57},
  year         = {2010},
}