Calibration of a microdialysis sensor and recursive glucose level estimation in ICU patients using Kalman and particle filtering

Charalampidis, Alexandros C.; Pontikis, Konstantinos; Mitsis, Georgios D.; Dimitriadis, George; Lampadiari, Vaia; Marmarelis, Vasilis Z.; Armaganidis, Apostolos; Papavassilopoulos, George P.

doi:10.1016/j.bspc.2015.11.003

Cited by 3 publications

(3 citation statements)

References 36 publications

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“…( 9) and the prior belief p (x k |z 1:k−1 )is the result of the prediction step in Eq. (11). Expressions ( 11) and ( 12) usually does not admit a closedform solution and thus cannot be computed exactly and require resorting to approximate methods.…”

Section: Overview Of Bayes' Filtersmentioning

confidence: 99%

“…To improve state estimation of a diabetic patient, plasma insulin concentration has recently been estimated from BG data using Bayesian filtering techniques which allow improving the estimation of glycemic conditions in real-time [9,10]. Filtering techniques are based on the proper combination of a dynamic model of the system and a state observer and have enjoyed remarkable success in the estimation of hidden states for different types of biomedical systems [11,12].…”

Section: Introductionmentioning

confidence: 99%

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Robust insulin estimation under glycemic variability using Bayesian filtering and Gaussian process models

Ávila

Paula

Martínez

et al. 2018

Biomedical Signal Processing and Control

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Section: Overview Of Bayes' Filtersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust insulin estimation under glycemic variability using Bayesian filtering and Gaussian process models

Ávila

Paula

Martínez

et al. 2018

Biomedical Signal Processing and Control

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“…The EKF gives an approximation of the optimal estimate. In order to approximate the non-linearities of the system dynamics, a linearized version of the nonlinear system model around the last state estimate is created (Maybeck, 1982, Charalampidis and Papavassilopoulos, 2011, Charalampidis et al, 2016.…”

Section: Extended Kalman Filtermentioning

confidence: 99%

Comparing Methods for Parameter Estimation of the Gompertz Tumor Growth Model

et al. 2017

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Cancer, also known as malignant tumor or malignant neoplasm, is the name given to a collection of related diseases. In all types of cancer, some of the body's cells begin to divide abnormally without stopping and have the potential to invade surrounding tissues. In this work, we focus on estimating the parameters of a model which tries to describe the growth of a cancer tumor based on the available measurements of the tumor volume and on comparing the effectiveness with respect to the accuracy of the estimation of the various methods we have tested. The Gompertz function is used as the model basis, and our analysis aims to compute the growth rate and the plateau size of the tumor. The methods used to estimate these parameters are based on least squares, maximum likelihood and the Extended Kalman Filter (EKF). In this work, we present five different methods. The results show that, when the process and measurement noise characteristics are known, maximizing the joint probability density function of the observations using numerical integration to compute the probability density functions yields most times the best results. The methods based on the EKF also yield satisfactory results.

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An OGI model for personalized estimation of glucose and insulin concentration in plasma

Wang

Geng

et al. 2021

MBE

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<abstract><p>Plasma glucose concentration (PGC) and plasma insulin concentration (PIC) are two essential metrics for diabetic regulation, but difficult to be measured directly. Often, PGC and PIC are estimated from continuous glucose monitoring and insulin delivery data. Nevertheless, the inter-individual variability and external disturbance (e.g. carbohydrate intake) bring challenges for accurate estimations. This study is to estimate PGC and PIC adaptively by identifying personalized parameters and external disturbances. An observable glucose-insulin (OGI) dynamic model is established to describe insulin absorption, glucose regulation, and glucose transport. The model parameters and disturbances can be extended to observable state variables and be identified dynamically by Bayesian filtering estimators. Two basic Gaussian noise based Bayesian filtering estimators, extended Kalman filtering (EKF) and unscented Kalman filtering (UKF), are implemented. Recognizing the prevalence of non-Gaussian noise, in this study, two new filtering estimators: particle filtering with Gaussian noise (PFG), and particle filtering with mixed non-Gaussian noise (PFM) are designed and implemented. The proposed OGI model in conjunction with the estimators is evaluated using the data from 30 in-silico subjects and 10 human participants. For in-silico subjects, the OGI with PFM estimator has the ability to estimate PIC and PGC adaptively, achieving RMSE of PIC $ 9.49\pm3.81 $ mU/L, and PGC $ 0.89\pm0.19 $ mmol/L. For human, the OGI with PFM has the promise to identify disturbances ($ 95.46\%\pm0.65\% $ accurate rate of meal identification). OGI model provides a way to fully personalize the parameters and external disturbances in real time, and has potential clinical utility for artificial pancreas.</p></abstract>

show abstract

Calibration of a microdialysis sensor and recursive glucose level estimation in ICU patients using Kalman and particle filtering

Cited by 3 publications

References 36 publications

Robust insulin estimation under glycemic variability using Bayesian filtering and Gaussian process models

Robust insulin estimation under glycemic variability using Bayesian filtering and Gaussian process models

Comparing Methods for Parameter Estimation of the Gompertz Tumor Growth Model

An OGI model for personalized estimation of glucose and insulin concentration in plasma

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