Key result
Non-linear classification of autoregressive model coefficients from respiratory flow signals correctly identified breathing patterns in over 92% of chronic heart failure patients.
Why the study?
Does autoregressive modeling of respiratory flow signals accurately classify periodic vs non-periodic breathing in chronic heart failure patients?
Cross-Sectional (n=26)
Does autoregressive modeling of respiratory flow signals accurately classify periodic vs non-periodic breathing in chronic heart failure patients?
Absolute Event Rate: 92% vs 84.6%
Autoregressive modeling of respiratory flow signals combined with non-linear classification can accurately identify periodic breathing in chronic heart failure patients.
Supports non-invasive breathing pattern classification in chronic heart failure; hypothesis-generating and requires prospective validation before clinical use.
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.
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Garde et al. (2008) conducted a cross-sectional in Chronic heart failure (n=26). Autoregressive model of respiratory flow envelope signal vs. Linear classification was evaluated on Percentage of well-classified patients (periodic vs non-periodic breathing). Non-linear classification of autoregressive model coefficients from respiratory flow signals correctly identified breathing patterns in over 92% of chronic heart failure patients.
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