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Detection and Classification of Measurement Errors in Bioimpedance Spectroscopy

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  • David Ayllón
  • Roberto Gil-Pita
  • Fernando Seoane

Abstract

Bioimpedance spectroscopy (BIS) measurement errors may be caused by parasitic stray capacitance, impedance mismatch, cross-talking or their very likely combination. An accurate detection and identification is of extreme importance for further analysis because in some cases and for some applications, certain measurement artifacts can be corrected, minimized or even avoided. In this paper we present a robust method to detect the presence of measurement artifacts and identify what kind of measurement error is present in BIS measurements. The method is based on supervised machine learning and uses a novel set of generalist features for measurement characterization in different immittance planes. Experimental validation has been carried out using a database of complex spectra BIS measurements obtained from different BIS applications and containing six different types of errors, as well as error-free measurements. The method obtained a low classification error (0.33%) and has shown good generalization. Since both the features and the classification schema are relatively simple, the implementation of this pre-processing task in the current hardware of bioimpedance spectrometers is possible.

Suggested Citation

  • David Ayllón & Roberto Gil-Pita & Fernando Seoane, 2016. "Detection and Classification of Measurement Errors in Bioimpedance Spectroscopy," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-19, June.
    • Handle: RePEc:plo:pone00:0156522
    DOI: 10.1371/journal.pone.0156522
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    References listed on IDEAS

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    1. Brusco, Michael J., 2014. "A comparison of simulated annealing algorithms for variable selection in principal component analysis and discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 38-53.
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