njit-etd2003-081 - New Jersey Institute of Technology

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again repeated over and over for the entire duration of data collection. Hence each
COPD subject breathed at the random rate at which the subject was comfortable and still
gave a flat power spectrum over the frequencies of interest.
These boxes were used for convenience and simplicity without changing the
objective of broadband respiration signal generation.
4.2.10 Principal Component Analysis
The central idea of Principal component analysis (PCA) is to reduce the dimensionality
of the data set while retaining as much as possible the variation in the data set. Principal
components (PC's) are linear transformations of the original set of variables. PC's are
uncorrelated and ordered so that the first few PC's contain most of the variations in the
original data set [63]. The first PC has the geometric interpretation that it is a new set of
the coordinate axes that maximizes the variation of the projections of the data points
onto the new coordinate axes.
In this dissertation a data set obtained from real data and analysis results
(coherence, partial coherence) that contained 15 parameters is shown in Table 4.2. The
first three parameters were the actual rate values taken from acquired data of normal and
COPD subjects (i.e. heart rate in beats per minute, respiration in breaths per minute and
blood pressure in beats per minute). The other parameters were obtained from LF and
HF weighted values of the coherence/partial coherence cross-spectral analyses.