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Parameter Estimation of Nonlinear Biomedical Systems Using Extended Kalman Filter Algorithm: Development of Patient Specific Models

Parameter Estimation of Nonlinear Biomedical Systems Using Extended Kalman Filter Algorithm: Development of Patient Specific Models

Kamalanand Krishnamurthy
Copyright: © 2018 |Pages: 24
ISBN13: 9781522531586|ISBN10: 1522531580|EISBN13: 9781522531593
DOI: 10.4018/978-1-5225-3158-6.ch030
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MLA

Krishnamurthy, Kamalanand. "Parameter Estimation of Nonlinear Biomedical Systems Using Extended Kalman Filter Algorithm: Development of Patient Specific Models." Biomedical Engineering: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2018, pp. 690-713. https://doi.org/10.4018/978-1-5225-3158-6.ch030

APA

Krishnamurthy, K. (2018). Parameter Estimation of Nonlinear Biomedical Systems Using Extended Kalman Filter Algorithm: Development of Patient Specific Models. In I. Management Association (Ed.), Biomedical Engineering: Concepts, Methodologies, Tools, and Applications (pp. 690-713). IGI Global. https://doi.org/10.4018/978-1-5225-3158-6.ch030

Chicago

Krishnamurthy, Kamalanand. "Parameter Estimation of Nonlinear Biomedical Systems Using Extended Kalman Filter Algorithm: Development of Patient Specific Models." In Biomedical Engineering: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 690-713. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-3158-6.ch030

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Abstract

Parameter estimation is a central issue in mathematical modelling of biomedical systems and for the development of patient specific models. The technique of estimating parameters helps in obtaining diagnostic information from computational models of biological systems. However, in most of the biomedical systems, the estimation of model parameters is a challenging task due to the nonlinearity of mathematical models. In this chapter, the method of estimation of nonlinear model parameters from measurements of state variables, using the extended Kalman filter, is extensively explained using an example of the three-dimensional model of the HIV/AIDS system.

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