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At what data length do cerebral autoregulation measures stabilise?

At what data length do cerebral autoregulation measures stabilise?
At what data length do cerebral autoregulation measures stabilise?
Cerebral autoregulation is commonly assessed through mathematical models that use noninvasive measurements of arterial blood pressure and cerebral blood flow velocity. There is no agreement in the literature as to what is the minimum length of data needed for the cerebral autoregulation coefficients to stabilise. We introduce a simple empirical tool for studying the minimum length of time series needed to parameterise three popular cerebral autoregulation coefficients ARI, Mx and Phase (in the low frequency range [0.07-0.2] Hz), which can be easily applied in a more general context. We use our recently collected data, from which we select high quality (absence of non-physiological artefacts), baseline ABP-CBFV time series (16-minute each). The data were beat-to-beat averaged and downsampled at 10 Hz. On average, ARI exhibits greater variability than Mx and Phase, when calculated for short intervals; however, it stabilises fastest. Our results show that values of ARI, Mx and Phase calculated on intervals shorter than 3 min (1800 samples), 6 min (3600 samples) and 5 min (3000 samples), respectively, may be very sensitive to changes in the length of data interval.
0967-3334
Mahdi, Adam
01b21495-81ce-4207-a565-2ec344bf4b44
Nikolic, Dragana
772b3eb2-c994-440a-ab86-27e862bd39f7
Birch, Anthony A.
0a31c6be-d058-4ca4-a332-8dcff4a470ab
Payne, Stephen
457f9441-a08f-4396-bd63-1bba56b90d9a
Mahdi, Adam
01b21495-81ce-4207-a565-2ec344bf4b44
Nikolic, Dragana
772b3eb2-c994-440a-ab86-27e862bd39f7
Birch, Anthony A.
0a31c6be-d058-4ca4-a332-8dcff4a470ab
Payne, Stephen
457f9441-a08f-4396-bd63-1bba56b90d9a

Mahdi, Adam, Nikolic, Dragana, Birch, Anthony A. and Payne, Stephen (2017) At what data length do cerebral autoregulation measures stabilise? Physiological Measurement. (doi:10.1088/1361-6579/aa76a9).

Record type: Article

Abstract

Cerebral autoregulation is commonly assessed through mathematical models that use noninvasive measurements of arterial blood pressure and cerebral blood flow velocity. There is no agreement in the literature as to what is the minimum length of data needed for the cerebral autoregulation coefficients to stabilise. We introduce a simple empirical tool for studying the minimum length of time series needed to parameterise three popular cerebral autoregulation coefficients ARI, Mx and Phase (in the low frequency range [0.07-0.2] Hz), which can be easily applied in a more general context. We use our recently collected data, from which we select high quality (absence of non-physiological artefacts), baseline ABP-CBFV time series (16-minute each). The data were beat-to-beat averaged and downsampled at 10 Hz. On average, ARI exhibits greater variability than Mx and Phase, when calculated for short intervals; however, it stabilises fastest. Our results show that values of ARI, Mx and Phase calculated on intervals shorter than 3 min (1800 samples), 6 min (3600 samples) and 5 min (3000 samples), respectively, may be very sensitive to changes in the length of data interval.

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More information

Accepted/In Press date: 2 June 2017
e-pub ahead of print date: 27 June 2017

Identifiers

Local EPrints ID: 415289
URI: http://eprints.soton.ac.uk/id/eprint/415289
ISSN: 0967-3334
PURE UUID: aff779ca-eb00-4a9c-afcf-d69868a12c5c

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Date deposited: 06 Nov 2017 17:31
Last modified: 06 Oct 2020 23:49

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