Nonlinear analysis of the BOLD signal

Authors:
Zhenghui Hu;Xiaohu Zhao;Huafeng Liu;Pengcheng Shi
Affiliations:
State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou, China and B. Thomas Golisano College of Computing and Information Sciences, Rochester Institute of Technology ...;Department of Radiology, Tongji Hospital of Tongji University, Shanghai, China;Department of Optical Engineering, State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou, China;B. Thomas Golisano College of Computing and Information Sciences, Rochester Institute of Technology, Rochester, NY and University of Rochester Medical Center, Rochester, NY
Venue:
EURASIP Journal on Advances in Signal Processing - Special issue on statistical signal processing in neuroscience
Year:
2009

Citing 3
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Abstract

The linearized filtering approach to the hemodynamic system is limited in capturing the inherent nonlinearities of physiological systems. The nonlinear estimation method therefore should be thought of as a natural way to access the nonlinear data assimilation problem. In this paper, we present a nonlinear filtering algorithm which is computationally expensive compared to the existing linearization filtering algorithms, for hemodynamic data assimilation, to address the deficiencies inherent to linearization. Simultaneous estimation of the physiological states and the system parameters have been demonstrated in a simulated and real data. The method provides more reasonable inference about the parameters of models for hemodynamic data assimilation.