Impulsive Feedback Modeling of Levodopa Pharmacokinetics Subject to Intermittently Interrupted Gastric Emptying

Runvik, Håkan; Medvedev, Alexander; Kjellsson, Maria C.

doi:10.23919/acc45564.2020.9147668

Cited by 6 publications

(5 citation statements)

References 16 publications

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“…In the noise-free case, the estimation is based on the following properties of the optimization problem in (5), which are given in Proposition 1.…”

Section: Estimation Principlementioning

confidence: 99%

“…1, since the region of mixed impulse weights then covers a larger area and, in particular, includes (b * 1 , b * 2 ). A non-negativity constraint for the impulse weights is for this reason added to (5). In the absence of noise, this would leave the region above γ P unaffected, while rendering the residual sum of squares…”

Section: A Estimation Of B 1 and Bmentioning

confidence: 99%

“…To avoid additional assumptions, a pulsatile time series is modeled by a linear plant with impulsive input in this work. In closed loop, this setup was developed for modeling testosterone regulation in [4], while a similar model was employed for pharmacokinetic applications in [5]. The estimation of the input sequence and continuous plant parameters is treated.…”

Section: Introductionmentioning

confidence: 99%

“…1. The division of the b 1 -b 2 parameter space, relative to the true parameter values (b * 1 , b *2 ), in terms of the solutions to(5). Left: regions with guaranteed signs of impulse weights.…”

mentioning

confidence: 99%

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Input Sequence and Parameter Estimation in Impulsive Biomedical Models

Runvik

Medvedev

2022

2022 European Control Conference (ECC)

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A hybrid model for biomedical time series comprising a continuous second-order linear time-invariant system driven by an input sequence of positively weighted Dirac deltafunctions is considered. The problem of the joint estimation of the input sequence and the continuous system parameters from output measurements is investigated. A solution that builds upon and refines a previously published least-squares formulation is proposed. Based on a thorough analysis of the properties of the least-squares solution, improvements in terms of accuracy and ease of use are achieved on synthetic data, compared to the original algorithm.

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“…In the noise-free case, the estimation is based on the following properties of the optimization problem in (5), which are given in Proposition 1.…”

Section: Estimation Principlementioning

confidence: 99%

Section: A Estimation Of B 1 and Bmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Input Sequence and Parameter Estimation in Impulsive Biomedical Models

Runvik

Medvedev

2022

2022 European Control Conference (ECC)

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“…We term the considered signal model, where an unknown sequence of impulses drives a continuous linear time-invariant system of a given structure but with unknown parameters, an impulsive time series . The resulting estimation problem is, however, of a general nature and applications both to cortisol regulation ( 4 ) and to pharmacokinetics ( 5 ) have been considered in the past.…”

Section: Introductionmentioning

confidence: 99%

Impulsive time series modeling with application to luteinizing hormone data

Runvik

Medvedev²

2022

Front. Endocrinol.

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This work considers the estimation of impulsive time series pertaining to biomedical systems and, in particular, to endocrine ones. We assume a signal model in the form of the output of a continuous linear time-invariant system driven by a sequence of instantaneous impulses, which concept is utilized here, in particular, for modeling of the male reproductive hormone axis. An estimation method to identify the impulsive sequence and the continuous system dynamics from sampled measurements of the output is proposed. Hinging on thorough mathematical analysis, the method improves upon a previously developed least-squares algorithm by resolving the trade-off between model fit and input sparsity, thus removing the need for manual tuning of user-defined estimation algorithm parameters. Experiments with synthetic data and Markov chain Monte-Carlo estimation demonstrate the viability of the proposed method, but also indicate that measurement noise renders the estimation problem ill-posed, as multiple estimates along a curve in the parameter space yield similar fits to data. The method is furthermore applied to clinical luteinizing hormone data collected from healthy males and, for comparability, one female, with similar results. Comparison between the estimated and theoretical elimination rates, as well as simulation of the estimated models, demonstrate the efficacy of the method. The sensitivity of the impulse distribution to the estimated elimination rates is investigated on a subject-specific data subset, revealing that the input sequence and elimination rate estimates can be interdependent. The dose-dependent effect of a selective gonadotropin releasing hormone receptor antagonist on the frequency and weights of the estimated impulses is also analyzed; a significant impact of the medication on the impulse weights is confirmed. To demonstrate the feasibility of the estimation approach for other hormones with pulsatile secretion, the modeling of cortisol data sets collected from three female adolescents was performed.

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Structural Identifiability of a Third-order Continuous System under Impulsive Feedback

Runvik

Medvedev

2020

IFAC-PapersOnLine

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Impulsive Feedback Modeling of Levodopa Pharmacokinetics Subject to Intermittently Interrupted Gastric Emptying

Cited by 6 publications

References 16 publications

Input Sequence and Parameter Estimation in Impulsive Biomedical Models

Input Sequence and Parameter Estimation in Impulsive Biomedical Models

Impulsive time series modeling with application to luteinizing hormone data

Structural Identifiability of a Third-order Continuous System under Impulsive Feedback

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