title: Breathing pattern characterization in chronic heart failure patients using the respiratory flow signal.
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.
authors.affiliation: Department ESAII, Universitat Politècnica de Catalunya, Barcelona, Spain.
authors.forename: A
authors.initials: A
authors.lastname: Garde
authors.forename: L
authors.initials: L
authors.lastname: Sörnmo
authors.forename: R
authors.initials: R
authors.lastname: Jané
authors.forename: B F
authors.initials: BF
authors.lastname: Giraldo
mesh_terms: D000328:Adult; D000368:Aged; D001698:Biomedical Engineering; D016022:Case-Control Studies; D002639:Cheyne-Stokes Respiration / Q000209:etiology / Q000503:physiopathology; D005260:Female; D006333:Heart Failure / Q000150:complications / Q000503:physiopathology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D012119:Respiration; D012307:Risk Factors; D012815:Signal Processing, Computer-Assisted; D055815:Young Adult