TY - JOUR

T1 - Deconvolving the input to random abstract parabolic systems

T2 - A population model-based approach to estimating blood/breath alcohol concentration from transdermal alcohol biosensor data

AU - Sirlanci, Melike

AU - Rosen, I. G.

AU - Luczak, Susan E.

AU - Fairbairn, Catharine E.

AU - Bresin, Konrad

AU - Kang, Dahyeon

N1 - Funding Information:
*This research was supported in part by grants from the Alcoholic Beverage Medical Research Foundation and the National Institute on Alcohol Abuse and Alcoholism (R21AA017711 and R01AA026368, S.E.L. and I.G.R.), and (R01AA025969, C.E.F.). 4 Author to whom any correspondence should be addressed.
Publisher Copyright:
© 2018 IOP Publishing Ltd.

PY - 2018/11/9

Y1 - 2018/11/9

N2 - The distribution of random parameters in, and the input signal to, a distributed parameter model with unbounded input and output operators for the transdermal transport of ethanol are estimated. The model takes the form of a diffusion equation with the input, which is on the boundary of the domain, being the blood or breath alcohol concentration (BAC/BrAC), and the output, also on the boundary, being the transdermal alcohol concentration (TAC). Our approach is based on the reformulation of the underlying dynamical system in such a way that the random parameters are treated as additional spatial variables. When the distribution to be estimated is assumed to be defined in terms of a joint density, estimating the distribution is equivalent to estimating a functional diffusivity in a multi-dimensional diffusion equation. The resulting system is referred to as a population model, and well-established finite dimensional approximation schemes, functional analytic based convergence arguments, optimization techniques, and computational methods can be used to fit it to population data and to analyze the resulting fit. Once the forward population model has been identified or trained based on a sample from the population, the resulting distribution can then be used to deconvolve the BAC/BrAC input signal from the biosensor observed TAC output signal formulated as either a quadratic programming or linear quadratic tracking problem. In addition, our approach allows for the direct computation of corresponding credible bands without simulation. We use our technique to estimate bivariate normal distributions and deconvolve BAC/BrAC from TAC based on data from a population that consists of multiple drinking episodes from a single subject and a population consisting of single drinking episodes from multiple subjects.

AB - The distribution of random parameters in, and the input signal to, a distributed parameter model with unbounded input and output operators for the transdermal transport of ethanol are estimated. The model takes the form of a diffusion equation with the input, which is on the boundary of the domain, being the blood or breath alcohol concentration (BAC/BrAC), and the output, also on the boundary, being the transdermal alcohol concentration (TAC). Our approach is based on the reformulation of the underlying dynamical system in such a way that the random parameters are treated as additional spatial variables. When the distribution to be estimated is assumed to be defined in terms of a joint density, estimating the distribution is equivalent to estimating a functional diffusivity in a multi-dimensional diffusion equation. The resulting system is referred to as a population model, and well-established finite dimensional approximation schemes, functional analytic based convergence arguments, optimization techniques, and computational methods can be used to fit it to population data and to analyze the resulting fit. Once the forward population model has been identified or trained based on a sample from the population, the resulting distribution can then be used to deconvolve the BAC/BrAC input signal from the biosensor observed TAC output signal formulated as either a quadratic programming or linear quadratic tracking problem. In addition, our approach allows for the direct computation of corresponding credible bands without simulation. We use our technique to estimate bivariate normal distributions and deconvolve BAC/BrAC from TAC based on data from a population that consists of multiple drinking episodes from a single subject and a population consisting of single drinking episodes from multiple subjects.

KW - deconvolution

KW - distributed parameter systems

KW - linear semigroups of operators

KW - population model

KW - random abstract parabolic systems

KW - system identification

KW - transdermal alcohol biosensor

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U2 - 10.1088/1361-6420/aae791

DO - 10.1088/1361-6420/aae791

M3 - Article

AN - SCOPUS:85056868319

SN - 0266-5611

VL - 34

JO - Inverse Problems

JF - Inverse Problems

IS - 12

M1 - 125006

ER -