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Statistical physics approaches to un...

Chen, Zhi.

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Statistical physics approaches to understanding physiological signals.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Statistical physics approaches to understanding physiological signals./
Author:	Chen, Zhi.
Description:	170 p.
Notes:	Source: Dissertation Abstracts International, Volume: 66-08, Section: B, page: 4267.
Contained By:	Dissertation Abstracts International66-08B.
Subject:	Physics, General. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3186497
ISBN:	9780542284632

Statistical physics approaches to understanding physiological signals.
Chen, Zhi.

Statistical physics approaches to understanding physiological signals. - 170 p.

Source: Dissertation Abstracts International, Volume: 66-08, Section: B, page: 4267.

Thesis (Ph.D.)--Boston University, 2006.

This thesis applies novel statistical physics approaches to investigate complex mechanisms underlying some physiological signals related to human motor activity and stroke. The scale-invariant properties of motor activity fluctuations and the phase coupling between blood flow (BF) in the brain and blood pressure (BP) at the finger are studied. Both BF and BP signals are controlled by cerebral autoregulation, the impairment of which is relevant to stroke.

ISBN: 9780542284632Subjects--Topical Terms:

1018488
Physics, General.

Statistical physics approaches to understanding physiological signals.
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020 $a 9780542284632
035 $a (UnM)AAI3186497
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100 1 $a Chen, Zhi. $3 940397
245 1 0 $a Statistical physics approaches to understanding physiological signals.
300 $a 170 p.
500 $a Source: Dissertation Abstracts International, Volume: 66-08, Section: B, page: 4267.
500 $a Major Professor: H. Eugene Stanley.
502 $a Thesis (Ph.D.)--Boston University, 2006.
520 $a This thesis applies novel statistical physics approaches to investigate complex mechanisms underlying some physiological signals related to human motor activity and stroke. The scale-invariant properties of motor activity fluctuations and the phase coupling between blood flow (BF) in the brain and blood pressure (BP) at the finger are studied. Both BF and BP signals are controlled by cerebral autoregulation, the impairment of which is relevant to stroke.
520 $a Part I of this thesis introduces experimental methods of assessing human activity fluctuations, BF and BP signals. These signals are often nonstationary, i.e., the mean and the standard deviation of signals are not invariant under time shifts. This fact imposes challenges in correctly analyzing properties of such signals. A review of conventional methods and the methods from statistical physics in quantifying long-range power-law correlations (an important scale-invariant property) and phase coupling in nonstationary signals is provided.
520 $a Part II investigates the effects of trends, nonstationarities and applying certain nonlinear filters on the scale-invariant properties of signals. Nonlinear logarithmic filters are shown to change correlation properties of anti-correlated signals and strongly positively-correlated signals. It is also shown that different types of trends may change correlation properties and thus mask true correlations in the original signal. A "superposition rule" is established to quantitatively describe the relationship among correlation properties of any two signals and the sum of these two signals. Based on this rule, simulations are conducted to show how to distinguish the correlations due to trends and nonstationaries from the true correlations in the real world signals.
520 $a Part III investigates dynamics of human activity fluctuations. Results suggest that apparently random forearm motion possesses previously unrecognized dynamic patterns characterized by common distribution forms, scale-invariant and nonlinear Fourier-phase features. Such patterns are consistent among different individuals, measurements, and are unrelated to extrinsic factors or activity level.
520 $a Part IV explores dynamics of cerebral autoregulation by investigating BF and BP relationships. The change of cerebral autoregulation from a healthy state to a pathologic state is most clearly revealed by increased strength and temporal persistence of cross correlations between the instantaneous phases of BF and BP signals.
590 $a School code: 0017.
650 4 $a Physics, General. $3 1018488
650 4 $a Biology, Animal Physiology. $3 1017835
650 4 $a Biophysics, General. $3 1019105
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710 2 0 $a Boston University. $3 1017454
773 0 $t Dissertation Abstracts International $g 66-08B.
790 1 0 $a Stanley, H. Eugene, $e advisor
790 $a 0017
791 $a Ph.D.
792 $a 2006
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3186497