njit-etd2003-081 - New Jersey Institute of Technology

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both the normal and COPD subjects were evaluated. Each distribution: the short time
Fourier transform (STFT), the smoothed pseudo Wigner-Ville (SPWVD), the Choi-
Williams (CWD) and the Born-Jordan-Cohen (BJCD), has unique characteristics which
was shown to affect the amount of smoothing and the generation of cross-terms
differently. The same biological signals were also analyzed using five different types of
wavelets: the Morlet, Meyer, Daubechies 4, Mexican Hat and Haar wavelets. The
resulting 3D and contour plots were compared: first, visually and second, by using the
proposed correlation indices of the Wigner distribution, which was cited in the literature
[38] as the best distribution of the Cohen's class, and five presented wavelets. Although
four Cohen's class distributions and five wavelets were used to analyze the physiological
data, it was found that the Morlet wavelet was consistently proven to be the best timefrequency
technique for HRV, BPV and respiration analysis.
In all, the difficulty with TF analysis was the ability to extract the relevant
information. The resulting time-dependent spectra contained a lot of information and
knowing how to extract the information was crucial to the interpretation of the physiology.
By expanding the concept of spectral analysis of heart rate variability (HRV) into timefrequency
analysis, one was able to quantitatively assess the parasympathetic (HF) and
sympatho-vagal balance (LF:HF) changes as a function of time. As a result, the reassessment
of the autonomic nervous system during rapid changes was made. The results
also demonstrated that TF analysis provided temporal and spectral localization that could
not be revealed by the use of standard spectral methods. The five wavelets were chosen
for further application because of overcoming the drawbacks of the other distributions by