  The cerebral autoregulation system (CAS), is a mechanism which aims to
regulate pressure variations occurring in the cerebral circulatory system. At
present, there only exist invasive methods and, in turn, they are not used to
prevent cerebrovascular accidents. Nowadays, the emergent concept of m-Health
allows to use mobile devices to assist the cerebral autoregulation index (ARI).
For this, it is necessary to find novel models which allow to approximate the
ARI by using the blood pressure value. This work proposes a gray-box neural
model to find a relation between the arterial blood pressure (ABP) and the
cerebral blood flow velocity (CBFV) in order to obtain the ARI. Preliminary
results show good performance by using a phenomenological model in comparison
to the Aaslid-Tiecks model.
