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parameters, instead of estimating every point on the 2 impulse responses. This reduction
in the degree of freedom or in the number of parameters to be estimated leads to
smoother and more robust estimated impulse responses.
To select the number of basis functions {S1, S2}, the delays {Dx, Du}, and the
orders of generalizations {nx, nu} that best fit the data without adding too much
redundancy to the computation, we computed an estimation of y(n) from Equation 4.18
for all combinations of {S1, S2}ranging from 1 to 7, Dx (RSA delay) ranging from -2 to 1
seconds, Du (ABR delay) ranging from 0.5 to 2 seconds, and {nx, nu} ranging from 0
(Laguerre functions) to 6. The “optimal model” was then selected for the combination
which resulted in the minimum value of the minimum description length (MDL) (Barron
et al., 1998).
4.5. Linear Time-invariant Model Parameter Estimation
To estimate the linear time-invariant transfer functions, hABR and hRCC, from a 5-
minute baseline data section, instead of using Equation 4.1directly as a one step process,
we introduced a 3-step model parameter estimation procedure to improve the accuracy of
the model prediction. This technique allowed an adjustment of the confounding effect of
respiration on blood pressure when calculating the ABR impulse response, as well ass the
confounding effect of blood pressure on respiration when calculating the RCC impulse
response. The details of this estimation process, shown in Figure 4.10, are described in
the next section.
Object Description
| Title | Modeling of cardiovascular autonomic control in sickle cell disease |
| Author | Sangkatumvong, Suvimol "Ming" |
| Author email | sangkatu@usc.edu; mingsuvimol@hotmail.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Biomedical Engineering |
| School | Viterbi School of Engineering |
| Date defended/completed | 2011-03-03 |
| Date submitted | 2011 |
| Restricted until | Unrestricted |
| Date published | 2011-04-26 |
| Advisor (committee chair) |
Khoo, Michael C.K. Coates, Thomas |
| Advisor (committee member) |
Wood, John C. Meiselman, Herbert J. |
| Abstract | Sickle cell disease (SCD) is a genetic disorder that is characterized by recurrent episodes of vaso-occlusive crisis (VOC) from the sickling behavior of red blood cells. Currently, no technique can distinguish the cause or predict the occurrence of a crisis accurately and reliably. One area which has rarely been studied in SCD patients is their autonomic nervous system (ANS). Since the ANS is responsible for the moment-to-moment control of the vascular tone, we hypothesized that the ANS plays an important role in the initiation of their VOC. Computational techniques, including spectral analysis of HRV and a model which characterizes the dynamics of baroreflex and respiratory-cardiac coupling, were used to assess cardiovascular autonomic control in SCD patients and normal control (CTL) subjects. These analysis techniques were applied to responses elicited from the subjects during the application of non-invasive and easily reproducible physiological interventions, such as transient-controlled hypoxia and the cold face test.; Our results demonstrate impairment in the ANS in SCD patients. In particular, hypoxic responses in SCD subjects showed a significantly stronger parasympathetic withdrawal compared to the CTLs. Furthermore, the autonomic responses to the cold face stimulus in SCD subjects showed an absence of the shift to parasympathetic dominance, as evidenced in the CTLs. In addition to the HRV analysis, model-based assessment also revealed the absence of both arterial baroreflex and respiratory-cardiac coupling augmentations in SCD patients during the cold face stimulus, while in CTL subjects both mechanisms showed tendencies to increase during the test.; During the data analysis period, we noticed that spontaneous sighs triggered marked periodic drops in peripheral microvascular perfusion. While the sigh frequency was the same in both groups, the probability of a sigh inducing a perfusion drop was significantly higher in SCD subjects in comparison to the CTLs. Evidence for sigh-induced sympathetic nervous system dominance was seen in SCD subjects, but was not significant in CTL. HRV analysis suggested that the cardiac ANS responses to sighs are not different between the two groups, after adjusting for the effect of post-sigh respiration. However, the peripheral sympathetic response in SCD subjects appeared to be enhanced in this group relative to the CTLs; and, furthermore, sighs may play a role in initiation of vaso-occlusive events in this group of patients.; In brief, all assessments we performed in this study suggested that the ANS responses to perturbations in SCD patients are more biased toward parasympathetic withdrawal and sympathetic activation, compared to normal controls. The complete mechanism is still a topic of investigation. Thus far, we have shown a relationship between the degree of this abnormality and the degree of both the anemia and infection/inflammation. We suspect that a mechanism related to the inflammatory reflex might play an important role in the ANS impairment in this group of patients.; In conclusion, this study draws attention to an enhanced ANS-mediated peripheral sympathetic driven vasoconstriction in SCD that could increase red cell retention in the microvasculature, promoting vaso-occlusion. This cascade of events could be the mechanism which triggers the VOC. |
| Keyword | autonomic nervous system; heart rate variability; respiration; sickle cell disease; physiological system modeling; sympathetic; parasympathetic; minimal model; baroreflex; respiratory-cardiac coupling; hypoxia; sigh; cold face test; cardiovascular autonomic control |
| Geographic subject | medical facilities: Childrens' Hospital Los Angeles |
| Geographic subject (city or populated place) | Los Angeles |
| Geographic subject (state) | California |
| Geographic subject (country) | USA |
| Coverage date | 2005/2010 |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m3781 |
| Contributing entity | University of Southern California |
| Rights | Sangkatumvong, Suvimol "Ming" |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | cisadmin@lib.usc.edu |
| Filename | etd-Sangkatumvong-4436 |
| Archival file | uscthesesreloadpub_Volume23/etd-Sangkatumvong-4436.pdf |
Description
| Title | Page 107 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | 89 parameters, instead of estimating every point on the 2 impulse responses. This reduction in the degree of freedom or in the number of parameters to be estimated leads to smoother and more robust estimated impulse responses. To select the number of basis functions {S1, S2}, the delays {Dx, Du}, and the orders of generalizations {nx, nu} that best fit the data without adding too much redundancy to the computation, we computed an estimation of y(n) from Equation 4.18 for all combinations of {S1, S2}ranging from 1 to 7, Dx (RSA delay) ranging from -2 to 1 seconds, Du (ABR delay) ranging from 0.5 to 2 seconds, and {nx, nu} ranging from 0 (Laguerre functions) to 6. The “optimal model” was then selected for the combination which resulted in the minimum value of the minimum description length (MDL) (Barron et al., 1998). 4.5. Linear Time-invariant Model Parameter Estimation To estimate the linear time-invariant transfer functions, hABR and hRCC, from a 5- minute baseline data section, instead of using Equation 4.1directly as a one step process, we introduced a 3-step model parameter estimation procedure to improve the accuracy of the model prediction. This technique allowed an adjustment of the confounding effect of respiration on blood pressure when calculating the ABR impulse response, as well ass the confounding effect of blood pressure on respiration when calculating the RCC impulse response. The details of this estimation process, shown in Figure 4.10, are described in the next section. |
