Analysis of the respiratory flow cycle morphology in chronic heart failure patients applying principal components analysis.

Abstract

The study of flow cycle morphology provides new information about the breathing pattern. This study proposes the characterization of cycle morphology in chronic heart failure patients (CHF) patients, with periodic (PB) and non-periodic breathing (nPB) patterns, and healthy subjects. Principal component analysis is applied to extract a respiratory cycle model for each time segment defined by a 30-s moving window. To characterize morphology of the model waveform, a number of parameters are extracted whose significance is evaluated in terms of the following three classification problems: CHF patients with either PB or nPB, CHF patients versus healthy subjects, and nPB patients versus healthy subjects. 26 CHF patients (8 with PB and 18 with non-periodic breathing pattern (nPB)) and 35 healthy subjects are studied. The results show that a respiratory cycle compressed in time characterizes PB patients, i.e., shorter inspiratory and expiratory periods, and higher dispersion of the maximum inspiratory and expiratory flow value (accuracy of 87%). The maximal expiratory flow instant occurs earlier in CHF patients than in healthy subjects (accuracy of 87%), with a steeper slope between inspiration and expiration. It is also found that the standard deviation of the expiratory period, evaluated for each subject, is much lower in CHF patients than in healthy subjects. The maximal expiratory flow instant occurs earlier (accuracy of 84%) in nPB patients, when comparing subjects with similar respiratory pattern like nPB patients and healthy subjects.

• Authors: Ainara Garde, Beatriz F Giraldo, Leif Sörnmo, Raimon Jané
• Journal: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference (Annu Int Conf IEEE Eng Med Biol Soc) Vol. 2011 Pg. 1725-8 ( 2011) ISSN: 2694-0604 [Electronic] United States
• PMID: 22254659 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)

Topics

• Adult
• Algorithms
• Chronic Disease
• Computer Simulation
• Female
• Heart Diseases (diagnosis, physiopathology)
• Humans
• Male
• Models, Biological
• Models, Statistical
• Principal Component Analysis
• Respiratory Rate