Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

The dynamic relationship between cerebral blood flow, arterial blood pressure and arterial CO2 is studied using the Laguerre-Volterra network methodology for modeling multiple-input nonlinear systems. Spontaneous beat-to-beat cerebral blood flow velocity and mean arterial blood pressure fluctuations, as well as breath-to-breath end-tidal CO2 fluctuations are analyzed and the Volterra kernels of the system are obtained. It is found that, while pressure changes explain most of the blood flow velocity variations, the inclusion of end-tidal CO2 fluctuations as an additional input variable can improve the prediction accuracy of the model output considerably. The model includes also nonlinear interactions between pressure and end-tidal CO2 and their impact on cerebral blood flow.


Journal article


Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

Publication Date





1341 - 1342