njit-etd2003-081 - New Jersey Institute of Technology

CHAPTER 6
CONCLUSIONS
Recently, joint time-frequency signal representation has received considerable attention as
a powerful tool for analyzing a variety of signals and systems of biological origin, which
do not comply with the stationarity assumptions. The time-frequency representations of
signals can be classified as linear and quadratic. The linear time-frequency representations
(TFR) include the short-time Fourier transform (STFT) and the wavelet transform. All
linear TFR's satisfy the superposition or linearity principle. On the other hand, the
quadratic TFR's provide a time-frequency energy distribution or instantaneous power
spectrum [27]. They are called quadratic since the energy is a quadratic signal
representation. An energy distribution-based TF representation combines the
instantaneous power and the spectral energy density. The temporal and spectral
correlations can also be combined as an alternative quadratic representation [27].
In reality signals may have unknown spectral components. In that case, TFR's
may suffer significantly from cross-terms of these spectral components. This problem
may be partially overcome by designing specific kernels for the signal at hand. In this
dissertation, the author explored the possibility of better representation of three particular
biological signals, heart rate variability, blood pressure variability and respiration. The
author evaluated the use of time-frequency analysis to investigate the physical and
respiratory effects attributed to COPD.
In the first phase of this work, the application of five different bilinear
representations on modeled HRV test signals, and experimental HRV and BPV signals of
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