Key result
Continuous-discrete extended Kalman filter successfully estimates nonlinear hemodynamic states and BOLD signals across parameter variations.
Introduces a stochastic hemodynamic system using an extended Kalman filter to model brain neural activity for potential use in clinical monitoring platforms like fNIRS.
Hypothesis-generating for fNIRS hemodynamic monitoring; leaves open prospective clinical validation before any adoption.
This paper introduces a stochastic hemodynamic system to describe the brain neural activity based on the balloon model. A continuous-discrete extended Kalman filter is used to estimate the nonlinear model states. The stability, controllability and observability of the proposed model are described based on the simulation and measurement data analysis. The observability and controllability characteristics are in- troduced as significant factors to validate the preference of different hemodynamic factors to be considered for diagnosis and monitoring in clinical applications. This model also can be efficiently applied in any monitoring and control platform include brain and for study of hemodynamics in brain imaging modalities such as pulse oximetry and functional near infrared spectroscopy. The work is on progress to extend the proposed model to cover more hemodynamic and neural brain signals for real-time in-vivo application.
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Kamrani et al. (2012) studied Brain neural activity (hemodynamic modeling). Extended Kalman filter with balloon model was evaluated on Estimation of BOLD signal and hemodynamic states. The proposed continuous-discrete extended Kalman filter applied to a stochastic balloon model successfully estimated nonlinear hemodynamic states and BOLD signals, demonstrating stability across parameter variations.
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