njit-etd2003-081 - New Jersey Institute of Technology

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5.7 Cluster Analysis
The purpose of using principal component analysis (PCA) and cluster analysis (CA) is
illustrated by the results of figures 5.63 and 5.64 and summarized in Table 5.10. First,
PCA-CA was used to blindly separate the normal subjects and COPD patients using a
data set that contained the physiological data such as heart rate, blood pressure rate and
respiration rate as well as the cross-spectral results of the (weighted) coherence and
partial coherence in both the LF and HF range. Figure 5.63 clearly shows the separation
between the COPD group (green squares and blue pluses) from the normal group (red
circles). In the same figure, one red circle of a normal subject was placed near a blue plus
and a green square of the COPD subject. The reason for this may be that the COPD
subjects represented by blue plus and green square may only be at risk of the disease.
Hence, their physiological parameters placed them near that of the normal subject. The
COPD group is represented in green and blue markers because their data came from the
same COPD group, but were from two different testing trials. This proves that cluster
analysis could separate the normal and COPD subjects and the results were reproducible,
i.e. same results for the same COPD group even with different data sets from different
experimental trials.
Figure 5.64 shows the results of not only normal and COPD blind separation but
also the COPD severity classification. When the desired number of clusters was set prior
to running the cluster analysis program to separate the normal group (cluster 1) from the
"at risk" group (cluster 2), the "mild' group (cluster 3), the "moderate" group (cluster 4)
and the "severe' group (cluster 5), the result was the graph of figure 5.64. This is the first
attempt at this type of blind separation and classification. Another more fine-tuned