njit-etd2003-081 - New Jersey Institute of Technology

In the second part of the study, the Morlet, Meyer, Daubechies 4, Mexican Hat
and Haar wavelets are used to investigate the heart rate and blood pressure variability
from both COPD and normal subjects. The results of wavelet analysis give much more
useful information than the Cohen's class representations. Here we are able to
quantitatively assess the parasympathetic (HF) and sympatho-vagal balance (LF:HF)
changes as a function of time. As a result, COPD subjects breathe faster, have higher
blood pressure variability and lower HRV.
In the third part of the study, a special class of the exogenous autoregressive
(ARX) model is developed as an analytical tool for uncovering the hidden autonomic
control processes. Non-parametric relationships between the input and outputs of the
ARX model resulting in transfer function estimations of the noise filters and the input
filter were used as mechanistic cardiovascular models that have shown to have predictive
capabilities for the underlying autonomic nervous system activity of COPD patients.
Transfer functions of COPD cardiovascular models have similar DC gains but show a
larger lag in phase as compared to the models of normal subjects.
Finally, a method of severity classification is presented. This method combines
the techniques of principal component analysis (PCA) and cluster analysis (CA) and has
been shown to separate the COPD from the normal population with 100% accuracy. It
can also classify the COPD population into "at risk", "mild", "moderate" and "severe"
stages with 100%, 90%, 88% and 100% accuracy respectively. As a result, cluster and
principal component analysis can be used to separate COPD and normal subjects and can
be used successfully in COPD severity classification.