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Non-linear models for the detection of impaired cerebral blood flow autoregulation

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  • Max Chacón
  • José Luis Jara
  • Rodrigo Miranda
  • Emmanuel Katsogridakis
  • Ronney B Panerai

Abstract

The ability to discriminate between normal and impaired dynamic cerebral autoregulation (CA), based on measurements of spontaneous fluctuations in arterial blood pressure (BP) and cerebral blood flow (CBF), has considerable clinical relevance. We studied 45 normal subjects at rest and under hypercapnia induced by breathing a mixture of carbon dioxide and air. Non-linear models with BP as input and CBF velocity (CBFV) as output, were implemented with support vector machines (SVM) using separate recordings for learning and validation. Dynamic SVM implementations used either moving average or autoregressive structures. The efficiency of dynamic CA was estimated from the model’s derived CBFV response to a step change in BP as an autoregulation index for both linear and non-linear models. Non-linear models with recurrences (autoregressive) showed the best results, with CA indexes of 5.9 ± 1.5 in normocapnia, and 2.5 ± 1.2 for hypercapnia with an area under the receiver-operator curve of 0.955. The high performance achieved by non-linear SVM models to detect deterioration of dynamic CA should encourage further assessment of its applicability to clinical conditions where CA might be impaired.

Suggested Citation

  • Max Chacón & José Luis Jara & Rodrigo Miranda & Emmanuel Katsogridakis & Ronney B Panerai, 2018. "Non-linear models for the detection of impaired cerebral blood flow autoregulation," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-16, January.
  • Handle: RePEc:plo:pone00:0191825
    DOI: 10.1371/journal.pone.0191825
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    References listed on IDEAS

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    1. Hu, Kun & Peng, C.K. & Huang, Norden E. & Wu, Zhaohua & Lipsitz, Lewis A. & Cavallerano, Jerry & Novak, Vera, 2008. "Altered phase interactions between spontaneous blood pressure and flow fluctuations in type 2 diabetes mellitus: Nonlinear assessment of cerebral autoregulation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(10), pages 2279-2292.
    2. Lenth, Russell V., 2016. "Least-Squares Means: The R Package lsmeans," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 69(i01).
    3. Max Chacón & José Luis Jara & Ronney B Panerai, 2014. "A New Model-Free Index of Dynamic Cerebral Blood Flow Autoregulation," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-11, October.
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