Distributed parameter model based system identification and filtering in the estimation of blood alcohol concentration from transdermal alcohol biosensor data

Methods for the estimation of blood or breath alcohol concentration (BAC/BrAC) from biosensor measured transdermal alcohol concentration (TAC) are developed, evaluated and compared. Specifically, a scheme based on a distributed parameter model with unbounded input and output for ethanol transport in the skin is compared to more conventional filtering/deconvolution techniques, one based on frequency domain methods, and the other on a time series approach using an autoregressive moving average (ARMA) input/output model. Our basis for comparison are five statistics of interest to alcohol researchers and clinicians: peak BAC/BrAC, time of peak BAC/BrAC, the ascending and descending slopes of the BAC/BrAC curve and area underneath the BAC/BrAC curve. It can be shown that the ARMA-based method yields the best estimation of the peak while the distributed parameter model produces the best estimation of the time of the peak. The Fourier-based method has the least variance out of the three and is computationally very efficient.