Characterization of periodic and non-periodic breathing pattern in chronic heart failure patients.
Periodic breathing (PB) has a high prevalence in chronic heart failure (CHF) patients with mild to moderate symptoms and poor ventricular function. This work proposes the analysis and characterization of the respiratory pattern to identify periodic breathing pattern (PB) and non-periodic breathing pattern (nPB) through the respiratory flow signal. The respiratory pattern analysis is based on the extraction and the study of the flow envelope signal. The flow envelope signal is modelled by an autoregressive model (AR) whose coefficients would characterize the respiratory pattern of each group. The goodness of the characterization is evaluated through a linear and non linear classifier applied to the AR coefficients. An adaptive feature selection is used before the linear and non linear classification, employing leave-one-out cross validation technique. With linear classification the percentage of well classified patients (8 PB and 18 nPB patients) is 84.6% using the statistically significant coefficients whereas with non linear classification, the percentage of well classified patients increase to more than 92% applying the best subset of coefficients extracted by a forward selection algorithm.
Garde
Ainara
A
Dep. of ESAII, Universitat Politécnica de Catalunya (UPC), Institut de Bioingenyeria de Catalunya (IBEC) and CIBER de Bioingenieréa, Biomateriales y Nanomedicina (CIBER-BBN), Barcelona, Spain. Ainara.Garde@upc.edu
Giraldo
Beatriz F
BF
Jané
Raimon
R
Díaz
Iván
I
Herrera
Sergio
S
Benito
Salvador
S
Domingo
Maite
M
Bayés-Genis
Antonio
A
D000465:Algorithms; D002908:Chronic Disease; D006333:Heart Failure / Q000175:diagnosis* / Q000473:pathology* / Q000503:physiopathology*; D006801:Humans; D015233:Models, Statistical; D008962:Models, Theoretical; D012044:Regression Analysis; D015203:Reproducibility of Results; D012119:Respiration; D015656:Respiratory Mechanics / Q000502:physiology*; D012132:Respiratory Muscles / Q000503:physiopathology*; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted