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.
[1]
W. A. Sandham,et al.
Improved PCA-Based Electrocardiogram Data Compression Using Variable-Length Asymmetric Beat Vectors
,
1999
.
[2]
B. F. Giraldo,et al.
Breathing Pattern Characterization in Chronic Heart Failure Patients Using the Respiratory Flow Signal
,
2010,
Annals of Biomedical Engineering.
[3]
A. Bahammam,et al.
Cheyne-Stokes Respiration in Patients with Heart Failure
,
2009,
Lung.
[4]
R. Maestri,et al.
Nocturnal periodic breathing is an independent predictor of cardiac death and multiple hospital admissions in Heart failure
,
2006,
2006 Computers in Cardiology.
[5]
I. Jolliffe.
Principal Component Analysis
,
2002
.
[6]
R Colombo,et al.
Prognostic value of nocturnal Cheyne-Stokes respiration in chronic heart failure.
,
1999,
Circulation.
[7]
Pablo Laguna,et al.
Principal Component Analysis in ECG Signal Processing
,
2007,
EURASIP J. Adv. Signal Process..
[8]
Raimon Jané,et al.
Correntropy-Based Spectral Characterization of Respiratory Patterns in Patients With Chronic Heart Failure
,
2010,
IEEE Transactions on Biomedical Engineering.