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REAL-TIME RECURSIVE ESTIMATION OF, AND UNCERTAINTY QUANTIFICATION FOR, BREATH ALCOHOL CONCENTRATION VIA LQ TRACKING CONTROL-BASED INVERSE FILTERING OF TRANSDERMAL ALCOHOL BIOSENSOR SIGNALS.

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Related Experiment Video

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Comparing a Distributed Parameter Model-Based System Identification Technique with More Conventional Methods for

Jian Li1, Susan E Luczak2, I G Rosen3

  • 1Department of Electrical Engineering Systems, University of Southern California, 3740 McClintock Ave. EEB 424, Los Angeles 90089, USA.

Journal of Inverse and Ill-Posed Problems
|December 31, 2019
PubMed
Summary
This summary is machine-generated.

This study compares three methods for estimating blood alcohol concentration (BAC) from skin alcohol measurements. A novel system identification method shows promise for accurate BAC/BrAC estimation.

Keywords:
35K9047D0665M3292C5593B3093C20Blind deconvolutionDistributed parameter systemsFilteringSee www.ams.org/mscSystem identificationTransdermal alcohol biosensor

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Area of Science:

  • Biomedical Engineering
  • Analytical Chemistry
  • Pharmacokinetics

Background:

  • Accurate estimation of blood or breath alcohol concentration (BAC/BrAC) is crucial for clinical and research applications.
  • Transdermal alcohol concentration (TAC) measured by biosensors offers a non-invasive alternative to traditional methods.
  • Estimating BAC/BrAC from TAC involves solving an ill-posed inverse problem, requiring robust deconvolution techniques.

Purpose of the Study:

  • To evaluate and compare three distinct methods for estimating BAC/BrAC from biosensor-measured TAC.
  • To assess the performance of a novel system identification/quasi-blind deconvolution scheme against conventional techniques.
  • To determine which method best captures key pharmacokinetic parameters of alcohol distribution.

Main Methods:

  • A system identification approach using a distributed parameter model for ethanol transport in the skin.
  • Comparison with two conventional methods: frequency domain deconvolution and time series analysis (ARMA model).
  • Evaluation based on five statistical measures: peak BAC/BrAC, time of peak, ascending/descending slopes, and area under the curve.

Main Results:

  • The novel system identification method demonstrated comparable or superior performance in estimating key BAC/BrAC parameters.
  • All evaluated methods provided estimates for peak BAC/BrAC, time of peak, slopes, and area under the curve.
  • The distributed parameter model offered a potentially more accurate representation of skin ethanol transport dynamics.

Conclusions:

  • The system identification/quasi-blind deconvolution scheme is a viable and potentially advantageous method for estimating BAC/BrAC from TAC.
  • This approach may improve the accuracy and reliability of non-invasive alcohol monitoring.
  • Further validation is warranted to establish its clinical utility in diverse populations and conditions.