Generalized linear least squares algorithm for non-uniformly sampled biomedical system identification with possible repeated eigenvalues

Wong, Koon-Pong; Feng, Dagan; Siu, Wan-Chi

doi:10.1016/s0169-2607(98)00035-2

Cited by 9 publications

(7 citation statements)

References 18 publications

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“…However, the use of these techniques require the extraction of apriori signal values with uniform sampling and hence, data obtained from dynamic sources may not be suitable candidates for TF analysis. This necessitates the design of signal-specific methods [22] for efficient gait characterization. As a corrective measure, in order to overcome the disadvantages associated with non-uniform sampling, the following pre-processing steps are included:…”

Section: Pre-processingmentioning

confidence: 99%

Wavelet-based characterization of gait signal for neurological abnormalities

et al. 2015

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Section: Pre-processingmentioning

confidence: 99%

Wavelet-based characterization of gait signal for neurological abnormalities

et al. 2015

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“…3) Parameter Estimates: GLLS has been found useful in nonuniformly and uniformly sampled biomedical signal processing and parameter estimation [11]- [13]. Unbiased parameters can be obtained by GLLS through linearization of the differential equations without the need to specify initial parameters.…”

Section: ) Cluster Analysis Of Reconstructed Datamentioning

confidence: 99%

“…The generalized linear least square (GLLS) method has been proposed as a computationally efficient method to estimate individual kinetic parameters and physiological parameters without the need to specify initial parameters [11]- [13]. Thus, GLLS is potentially suitable to generate parametric images from dynamic SPECT studies.…”

mentioning

confidence: 99%

Fast and Reliable Estimation of Multiple Parametric Images Using an Integrated Method for Dynamic SPECT

Wen

Eberl

Feng

et al. 2007

IEEE Trans. Med. Imaging

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Abstract-Dynamic single photon emission computed tomography (SPECT) has demonstrated the potential to quantitatively estimate physiological parameters in the brain and the heart. The generalized linear least square (GLLS) method is a well-established method for solving linear compartment models with fast computational speed. However, the high level of noise intrinsic in the SPECT data leads to reliability and instability problems of GLLS for generating parametric images. An integrated method is proposed to restrict the noise in both the temporal and spatial domains to estimate multiple parametric images for dynamic SPECT. This method comprises three steps which are optimum image sampling schedule in the projection space, cluster analysis applied postreconstruction and parametric image generation with GLLS.

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“…Note, if the initial conditions on input and output and their higher derivatives are unknown the algorithm is modified so that these conditions are parameters to be determined [8]. Additionally, if roots of the polynomial A (k−1) (s) at any step are no longer distinct the algorithm is ammended appropriately [16].…”

Section: Derivation Of Gllsmentioning

confidence: 99%

On the Convergence of the Generalized Linear Least Squares Algorithm

Negoita

Renaut

2005

Bit Numer Math

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This paper considers the issue of parameter estimation for biomedical applications using nonuniformly sampled data. The generalized linear least squares (GLLS) algorithm, first introduced by Feng and Ho (1993), is used in the medical imaging community for removal of bias when the data defining the model are correlated. GLLS provides an efficient iterative linear algorithm for the solution of the non linear parameter estimation problem. This paper presents a theoretical discussion of GLLS and introduces use of both Gauss Newton and an alternating Gauss Newton for solution of the parameter estimation problem in nonlinear form. Numerical examples are presented to contrast the algorithms and emphasize aspects of the theoretical discussion. (2000): 65F10. AMS subject classification

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Generalized linear least squares algorithm for non-uniformly sampled biomedical system identification with possible repeated eigenvalues

Cited by 9 publications

References 18 publications

Wavelet-based characterization of gait signal for neurological abnormalities

Wavelet-based characterization of gait signal for neurological abnormalities

Fast and Reliable Estimation of Multiple Parametric Images Using an Integrated Method for Dynamic SPECT

On the Convergence of the Generalized Linear Least Squares Algorithm

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