Correntropy-based spectral characterization of respiratory patterns in patients with chronic heart failure.

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%.

• Authors: Ainara Garde, Leif Sörnmo, Raimon Jané, Beatriz F Giraldo
• Journal: IEEE transactions on bio-medical engineering (IEEE Trans Biomed Eng) Vol. 57 Issue 8 Pg. 1964-72 (Aug 2010) ISSN: 1558-2531 [Electronic] United States
• PMID: 20211799 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)

Topics

• Adult
• Algorithms
• Chronic Disease
• Computer Simulation
• Female
• Heart Failure (physiopathology)
• Humans
• Male
• Nonlinear Dynamics
• Pattern Recognition, Automated (methods)
• Reproducibility of Results
• Respiratory Function Tests (methods)
• Respiratory Mechanics (physiology)
• Signal Processing, Computer-Assisted