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|Title:||Digital Signal Processing of Hemodynamic Singals|
|Department:||Electrical and Computer Engineering|
|Abstract:||Physiological signals when subjected to digital signal processing algorithms often reveal information about their origin and how they are regulated. Recently, it has been shown that when power spectrum of the heart rate variability signal is computed, physiological mechanisms about how the autonomic nervous system modulates the sinus node of the heart can be unraveled. During the past several years, computation of the power spectrum of heart rate variability has progressed from Blackman-Tukey algorithm and autoregressive modelling to Wigner-Ville distribution. In this thesis, we describe the development of appropriate algorithms for QRS detection from an ECG signal to obtain a heart rate signal, interpolation of heart rate variability signal and the computation of power spectrum. We also describe mathematical details underlying time-frequency analysis, specifically for the Wigner-Ville distribution. We present a software package in C++, for computing the Wigner-Ville distribution of the heart rate variability signal. As applications of these methods in physiology and clinical medicine, we found that the power spectrum of the heart rate variability of premature infants can help us understand the ontogeny of the autonomic nervous system. Similarly, physiological effects of atropine, methacholine and allergen challenges can be elucidated using the power spectrum of heart rate variability in small animals, such as a rat model. Furthermore, a progressive tilt model in human subjects is used to compare power spectrum obtained from the Blackman-Tukey method, autoregressive modelling and the Wigner-Ville distribution. Finally, an application of the Wigner-Ville distribution technique to study the changes that take place in the autonomic regulation of the heart during different stages of sleep is presented.|
|Appears in Collections:||Digitized Open Access Dissertations and Theses|
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|hu_anne_x-a_1999Feb_masters.pdf||43.3 MB||Adobe PDF||View/Open|
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