A modified approach to objective surface generation within the Gauss-Newton parameter identification to ignore outlier data points

Gray, Rebecca; Docherty, Paul; Fisk, Liam; Murray, Rua

doi:10.1016/j.bspc.2016.06.009

Cited by 13 publications

(9 citation statements)

References 17 publications

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“…(6*) Gray et al (2015) modified Eq. 6 by modulating the magnitude of the residual vector and thus changing the weighting given to each data point in the iterations.…”

Section: Methodsmentioning

confidence: 99%

Identifying pressure dependent elastance in lung mechanics with reduced influence of unmodelled effects

et al. 2015

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The selection of optimal positive end expiratory pressure (PEEP) levels during ventilation therapy of patients with ARDS (acute respiratory distress syndrome) remains a problem for clinicians. One particular mooted strategy states that minimizing the energy transferred to the lung by mechanical ventilation could potentially be used to determine the optimal PEEP level. This minimization could potentially be undertaken by finding the minimum range of dynamic elastance.In this study, we compare an adapted Gauss-Newton method with the typical gauss newton method in terms of the level of agreement obtained in elastance-pressure curves across different PEEP levels in 10 patients. The Gauss-Newton adaptation effectively ignored characteristics in the data that are un-modelled. The adapted method successfully determined regions of the data that were un-modelled, as expected. In ignoring this un-modelled behavior, the adapted method captured the desired elastance-pressure curves with more consistency than the typical least-squares Gauss Newton method.

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“…(6*) Gray et al (2015) modified Eq. 6 by modulating the magnitude of the residual vector and thus changing the weighting given to each data point in the iterations.…”

Section: Methodsmentioning

confidence: 99%

Identifying pressure dependent elastance in lung mechanics with reduced influence of unmodelled effects

et al. 2015

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“…Points with high model error due to unmodelled mixing dynamics can lead to inaccurate parameter identification. 11,12 In cases where parameter identification may be ill posed, appropriate use of penalty functions and/or regularization methods may improve convergence. 13,14 One method for dealing with unmodelled mixing behavior is to exclude sampling data for the 5 or 10 minute period following bolus administration as it can be assumed it takes this length of time for glucose and insulin boluses to adequately mix.…”

Section: Introductionmentioning

confidence: 99%

“…13,14 One method for dealing with unmodelled mixing behavior is to exclude sampling data for the 5 or 10 minute period following bolus administration as it can be assumed it takes this length of time for glucose and insulin boluses to adequately mix. 2,15 Another method adapted the Gauss-Newton gradient-descent parameter identification method to limit the influence of outliers, 12 where subsequent comparison of identified insulin sensitivity estimates to a standard modelling approach showed it effectively captured model parameters typically obscured by unmodelled mixing dynamics. 16,17 A third approach is to include an additional local mixing compartment in the model.…”

Section: Introductionmentioning

confidence: 99%

“…These 3 strategies have never been compared directly. This analysis compares 3 implementations of the same Dynamic Insulin Sensitivity and Secretion Test (DISST) model: 2 (1) the DISST model with an added local mixing compartment, (2) The DISST model identified using the adapted Levenberg Marquardt method of Gray et al, 12 and (3) the DISST model but excluding data sampled immediately (up to 10 min) after the bolus injection. The implementations aim to improve model fit and insulin sensitivity identification through different means.…”

Section: Introductionmentioning

confidence: 99%

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The Effects of Additional Local-Mixing Compartments in the DISST Model-Based Assessment of Insulin Sensitivity

Lam

Murray

Docherty

et al. 2021

J Diabetes Sci Technol

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Background: The identification of insulin sensitivity in glycemic modelling can be heavily obstructed by the presence of outlying data or unmodelled effects. The effect of data indicative of local mixing is especially problematic with models assuming rapid mixing of compartments. Methods such as manual removal of data and outlier detection methods have been used to improve parameter ID in these cases, but modelling data with more compartments is another potential approach. Methods: This research compares a mixing model with local depot site compartments with an existing, clinically validated insulin sensitivity test model. The Levenberg-Marquardt (LM) parameter identification method was implemented alongside a modified version (aLM) capable of operator-independent omission of outlier data in accordance with the 3 standard deviation rule. Three cases were tested: LM where data points suspected to be affected by incomplete mixing at the depot site were removed, aLM, and LM with the more complex mixing model. Results: While insulin parameters identified in the mixing model differed greatly from those in the DISST model, there were strong Spearman correlations of approximately 0.93 for the insulin sensitivity values identified across all 3 methods. The 2 models also showed comparable identification stability in insulin sensitivity estimation through a Monte Carlo analysis. However, the mixing model required modifications to the identification process to improve convergence, and still failed to converge to feasible parameters on 5 of the 212 trials. Conclusions: The mixing compartment model effectively captured the dynamics of mixing behavior, but with no significant improvement in insulin sensitivity identification.

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“…Curve fitting is one of the most powerful and most widely used analysis tools to pre- and post-process data [ 1 , 2 ], to remove outliers [ 3 , 4 ], to compare candidate models [ 5 ], and to examine the relationship between one or more predictors [ 6 , 7 , 8 ]. The parameterization of curve fitting is usually realized by minimizing linear or non-linear least-squares residuals [ 9 , 10 ], in which the Levenberg–Marquardt (LM) algorithm, also known as the damped least-squares method, is popular for fast convergence [ 11 ]. Whether the LM fitting results can reflect physical meaning is highly dependent on the quality of measured signal.…”

Section: Introductionmentioning

confidence: 99%

Membership Function-Weighted Non-Linear Fitting Method for Optical-Sensing Modeling and Reconstruction

Meng

Yuan

et al. 2018

Sensors

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Imprecise measurements present universally due to variability in the measurement error. We devised a very simple membership function to evaluate fuzzily the quality of optical sensing with a small dataset, where a normal distribution cannot be assumed. The proposed membership function was further used as a weighting function for non-linear curve fitting under expected mathematical model constraints, namely the membership function-weighted Levenberg–Marquardt (MFW-LM) algorithm. The robustness and effectiveness of the MFW-LM algorithm were demonstrated by an optical-sensing simulation and two practical applications. (1) In laser-absorption spectroscopy, molecular spectral line modeling was greatly improved by the method. The measurement uncertainty of temperature and pressure were reduced dramatically, by 53.3% and 43.5%, respectively, compared with the original method. (2) In imaging, a laser beam-profile reconstruction from heavy distorted observations was improved by the method. As the dynamic range of the infrared camera increased from 256 to 415, the detailed resolution of the laser-beam profiles increased by an amazing 360%, achieving high dynamic-range imaging to capture optical signal details. Therefore, the MFW-LM algorithm provides a robust and effective tool for fitting a proper physical model and precision parameters from low-quality data.

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A modified approach to objective surface generation within the Gauss-Newton parameter identification to ignore outlier data points

Cited by 13 publications

References 17 publications

Identifying pressure dependent elastance in lung mechanics with reduced influence of unmodelled effects

Identifying pressure dependent elastance in lung mechanics with reduced influence of unmodelled effects

The Effects of Additional Local-Mixing Compartments in the DISST Model-Based Assessment of Insulin Sensitivity

Membership Function-Weighted Non-Linear Fitting Method for Optical-Sensing Modeling and Reconstruction

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