Frequency-domain analysis of cerebral autoregulation from spontaneous fluctuations in arterial blood pressure

The dynamic relationship between spontaneous fluctuations of arterial blood pressure (ABP) and corresponding changes in cerebral blood flow velocity (CBFV) is studied in a population of 83 neonates. Static and dynamic methods are used to identify two subgroups showing either normal (group A, n = 23)...

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Bibliographic Details
Published in:Medical & biological engineering & computing 1998-05, Vol.36 (3), p.315-322
Main Authors: PANERAI, R. B, RENNIE, J. M, KELSALL, A. W. R, EVANS, D. H
Format: Article
Language:English
Subjects:
Biological and medical sciences
Blood pressure
Brain - blood supply
Flow velocity
Hemodynamics
Homeostasis
Humans
Infant, Newborn
Investigative techniques, diagnostic techniques (general aspects)
Medical sciences
Nervous system
Regional Blood Flow
Signal Processing, Computer-Assisted
Ultrasonic investigative techniques
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Summary:The dynamic relationship between spontaneous fluctuations of arterial blood pressure (ABP) and corresponding changes in cerebral blood flow velocity (CBFV) is studied in a population of 83 neonates. Static and dynamic methods are used to identify two subgroups showing either normal (group A, n = 23) or impaired (group B, n = 21) cerebral autoregulation. An FFT algorithm is used to estimate the coherence and transfer function between CBFV and ABP. The significance of the linear dependence between these two variables is demonstrated by mean values of squared coherence > 0.50 for both groups in the frequency range 0.02-0.50 Hz. However, group A has significantly smaller coherences than group B in the frequency ranges 0.02-0.10 Hz and 0.33-0.49 Hz. The phase response of group A is also significantly more positive than that of group B, with slopes of 9.3 +/- 1.05, and 1.80 +/- 1.2 rad Hz-1, respectively. The amplitude frequency response is also significantly smaller for group A in relation to group B for the frequency range 0.25-0.43 Hz. These results suggest that transfer function analysis may be able to identify different components of cerebral autoregulation and also provide a deeper understanding of recent findings by other investigators.
ISSN:0140-0118
1741-0444
DOI:10.1007/bf02522477