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Practical implementation of optimal experimental design using the fractional-order Fricke–Morse bioimpedance model

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  • Sebastià Bargues, Àngela
  • Polo Sanz, José-Luis
  • García-Camacha Gutiérrez, Irene
  • Martín Martín, Raúl

Abstract

This paper provides, for the first time, the application of the Optimal Experimental Design (OED) theory. Two algorithms for computing exact and approximate optimal designs have been adapted for the fractional-order Fricke–Morse circuit model (which is widely used to describe experimental bioimpedance data). Frequencies at which the impedance is measured are optimized, while reducing the measurement acquisition time and maximizing the information about the fractional-order electrical behaviour of the biological tissue. As a practical implementation of this methodology, for a sample of apple tissue, D-optimal approximate and exact designs are computed to obtain the best estimates of the parameters values according to a criteria. These designs were compared with the classical design commonly used by practitioners showing the efficiencies of the optimal designs. The application of OED theory to this type of problems opens up many possibilities for future research.

Suggested Citation

  • Sebastià Bargues, Àngela & Polo Sanz, José-Luis & García-Camacha Gutiérrez, Irene & Martín Martín, Raúl, 2023. "Practical implementation of optimal experimental design using the fractional-order Fricke–Morse bioimpedance model," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).
    • Handle: RePEc:eee:chsofr:v:170:y:2023:i:c:s0960077923002758
    DOI: 10.1016/j.chaos.2023.113374
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    References listed on IDEAS

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    1. Àngela Sebastià Bargues & José-Luis Polo Sanz & Raúl Martín Martín, 2022. "Optimal Experimental Design for Parametric Identification of the Electrical Behaviour of Bioelectrodes and Biological Tissues," Mathematics, MDPI, vol. 10(5), pages 1-16, March.
    2. Radoslav Harman & Lenka Filová & Peter Richtárik, 2020. "A Randomized Exchange Algorithm for Computing Optimal Approximate Designs of Experiments," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(529), pages 348-361, January.
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