Optimizing the assessment of autoregulation from black-box models
Optimizing the assessment of autoregulation from black-box models
Autoregulation mechanisms maintain blood flow approximately stable when blood pressure changes. Impairment in autoregulatory function can be identified by black-box modelling with blood pressure as input, and blood flow as output, using only spontaneous variability in both signals. The current paper addresses the issue of how to assess autoregulaton from the model parameters. We propose using a test input in the shape of a band-pass filtered impulse, that reflects the characteristics of blood pressure variations more accurately than the more commonly used step response. Features extracted from the relationship between the new impulse and the best-fit model response are shown to have better dynamic range and stability than a comparable index derived from the step response.
9780863419348
1-4
Simpson, D.M.
53674880-f381-4cc9-8505-6a97eeac3c2a
Birch, A.A.
6998ba08-f9c9-44bd-a666-08090c8e18dd
July 2008
Simpson, D.M.
53674880-f381-4cc9-8505-6a97eeac3c2a
Birch, A.A.
6998ba08-f9c9-44bd-a666-08090c8e18dd
Simpson, D.M. and Birch, A.A.
(2008)
Optimizing the assessment of autoregulation from black-box models.
In 4th IET International Conference on Advances in Medical, Signal and Information Processing (MEDSIP 2008).
IEEE.
.
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Conference or Workshop Item
(Paper)
Abstract
Autoregulation mechanisms maintain blood flow approximately stable when blood pressure changes. Impairment in autoregulatory function can be identified by black-box modelling with blood pressure as input, and blood flow as output, using only spontaneous variability in both signals. The current paper addresses the issue of how to assess autoregulaton from the model parameters. We propose using a test input in the shape of a band-pass filtered impulse, that reflects the characteristics of blood pressure variations more accurately than the more commonly used step response. Features extracted from the relationship between the new impulse and the best-fit model response are shown to have better dynamic range and stability than a comparable index derived from the step response.
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Published date: July 2008
Additional Information:
ISSN 0537-9989
Venue - Dates:
4th IET International Conference on Advances in Medical Signal and Information Processing (MEDSIP 2008), Santa Margherita Ligure, Italy, 2008-07-14 - 2008-07-16
Identifiers
Local EPrints ID: 63663
URI: http://eprints.soton.ac.uk/id/eprint/63663
ISBN: 9780863419348
PURE UUID: d9b15eaf-3748-4473-af24-0b92af5181a7
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Date deposited: 29 Oct 2008
Last modified: 06 Mar 2024 02:40
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Author:
A.A. Birch
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