Key result
Nonlinear autoregressive SVM accurately models cerebral hemodynamics, reaching ~0.91 correlation during hypercapnia.
Why the study?
Modeling the influences of arterial blood pressure and arterial CO2 on cerebral blood flow is essential to understand regulatory mechanisms and extract clinically relevant parameters.
SVM modeling of ABP/PaCO2 effects on CBFV may aid regulatory research; leaves open clinical parameter validation.
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.
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Chacón et al. (2010) studied Healthy (n=16). Non-linear multivariate autoregressive Support Vector Machine (SVM) models vs. Finite impulse response models was evaluated on Correlation coefficients of model performance. Non-linear multivariate autoregressive SVM models demonstrated superior performance for modeling cerebral hemodynamics, yielding correlation coefficients of 0.71 ± 0.11 at baseline and 0.91 ± 0.06 during 5% CO2.
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