Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.551645
Title: Wavelet phase dynamics of intracranial pressure monitoring
Author: Sheppard, Lawrence William
Awarding Body: Lancaster University
Current Institution: Lancaster University
Date of Award: 2010
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Abstract:
The functions of the brain and cardiovascular system incorporate oscillatory processes at several timescales, each with time and frequency variability. We use the Morlet wavelet transform to approach these time-variable oscillatory processes in terms of their amplitude and phase dynamics. We also introduce new methods based on phase coherence and information theory to identify relationships between phase time series. We analyse data recorded from subjects in coma at Ulleval University Hospital in Oslo, Norway. These subjects vary in their ability to regulate cerebral blood flow, and we investigate the features of this autoregulatory process. We find high wavelet energy in intracranial pressure around 0.03 Hz in subjects with functioning autoregulation of cerebral blood flow. The autoregulatory system incorporates sensory information about blood pressure into the regulation of cerebral vascular resistance. We apply the method of phase coherence to show that the activity of the smooth muscle in the cerebral vasculature produces an intracranial pressure signal with a phase shift relative to arterial blood pressure. The phase shift begins in the myogenic (=0.1 Hz) frequency regime and the degree of phase shift is related to the degree of autoregulation in the subject. We develop a new algorithm based on mutual information and surrogate testing to identify fundamental frequencies with high harmonic content in noisy, non-sinusoidal oscillations of nonlinear systems. We reconstruct such oscillations in the cardiac, respiratory, and myogenic regimes.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.551645  DOI: Not available
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