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. 2008 Mar 1;196(2):724-743.
doi: 10.1016/j.amc.2007.07.026.

Deconvolving an Estimate of Breath Measured Blood Alcohol Concentration from Biosensor Collected Transdermal Ethanol Data

M Dumett  1 , G Rosen, J Sabat, A Shaman, L Tempelman, C Wang, Rm Swift
Affiliations

Deconvolving an Estimate of Breath Measured Blood Alcohol Concentration from Biosensor Collected Transdermal Ethanol Data

M Dumett et al. Appl Math Comput. .

Abstract

Biosensor measurement of transdermal alcohol oncentration in perspiration exhibits significant variance from subject to subject and device to device. Short duration data collected in a controlled clinical setting is used to calibrate a forward model for ethanol transport from the blood to the sensor. The calibrated model is then used to invert transdermal signals collected in the field (short or long duration) to obtain an estimate for breath measured blood alcohol concentration. A distributed parameter model for the forward transport of ethanol from the blood through the skin and its processing by the sensor is developed. Model calibration is formulated as a nonlinear least squares fit to data. The fit model is then used as part of a spline based scheme in the form of a regularized, non-negatively constrained linear deconvolution. Fully discrete, steepest descent based schemes for solving the resulting optimization problems are developed. The adjoint method is used to accurately and efficiently compute requisite gradients. Efficacy is demonstrated on subject field data.

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Figures

FIGURE 1.1
FIGURE 1.1
The Giner WrisTAS V.
FIGURE 1.2
FIGURE 1.2
Breath and transdermal data for Subject 1 (left) and Subject 2 (right).
FIGURE 2.1
FIGURE 2.1
Two phase design of the data analysis system (left) and the Blood/Lung/Breath analyzer and Blood/Skin/TAS systems (right).
FIGURE 3.1
FIGURE 3.1
A schematic diagram the Skin and TAS model.
FIGURE 5.1
FIGURE 5.1
The fit of the model to Subject 1’s hospital data (left) and the fit of the model to Subject 2’s hospital data (right).
FIGURE 5.2
FIGURE 5.2
BrAC recovery for Subject 1 (left) and BrAC recovery for Subject 2 (right).
FIGURE 6.1
FIGURE 6.1
Deconvolution of Subject 1’s calibration data (left) and forward simulation of Subject 2’s field test using fit model (right).
FIGURE 6.2
FIGURE 6.2
Forward simulation of Subject 1’s (left) and Subject 2’s (right) field test using recovered and interpolated subject supplied measurements of BrAC.
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