Non-linear multivariate modeling of cerebral hemodynamics with autoregressive Support Vector Machines

Cerebral blood flow (CBF) is normally controlled by myogenic and metabolic mechanisms that can be impaired in different cerebrovascular conditions. Modeling the influences of arterial blood pressure (ABP) and arterial CO(2) (PaCO(2)) on CBF is an essential step to shed light on regulatory mechanisms and extract clinically relevant parameters. Support Vector Machines (SVM) were used to model the influences of ABP and PaCO(2) on CBFV in two different conditions: baseline and during breathing of 5% CO(2) in air, in a group of 16 healthy subjects. Different model structures were considered, including innovative non-linear multivariate autoregressive (AR) models. Results showed that AR models are significantly superior to finite impulse response models and that non-linear models provide better performance for both structures. Correlation coefficients for multivariate AR non-linear models were 0.71 ± 0.11 at baseline, reaching 0.91 ± 0.06 during 5% CO(2). These results warrant further work to investigate the performance of autoregressive SVM in patients with cerebrovascular conditions.