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Detection and Classification of Measurement Errors in Bioimpedance Spectroscopy
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,...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4928898/ https://www.ncbi.nlm.nih.gov/pubmed/27362862 http://dx.doi.org/10.1371/journal.pone.0156522 |
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author | Ayllón, David Gil-Pita, Roberto Seoane, Fernando |
author_facet | Ayllón, David Gil-Pita, Roberto Seoane, Fernando |
author_sort | Ayllón, David |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4928898 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-49288982016-07-18 Detection and Classification of Measurement Errors in Bioimpedance Spectroscopy Ayllón, David Gil-Pita, Roberto Seoane, Fernando PLoS One Research Article 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. Public Library of Science 2016-06-30 /pmc/articles/PMC4928898/ /pubmed/27362862 http://dx.doi.org/10.1371/journal.pone.0156522 Text en © 2016 Ayllón et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Ayllón, David Gil-Pita, Roberto Seoane, Fernando Detection and Classification of Measurement Errors in Bioimpedance Spectroscopy |
title | Detection and Classification of Measurement Errors in Bioimpedance Spectroscopy |
title_full | Detection and Classification of Measurement Errors in Bioimpedance Spectroscopy |
title_fullStr | Detection and Classification of Measurement Errors in Bioimpedance Spectroscopy |
title_full_unstemmed | Detection and Classification of Measurement Errors in Bioimpedance Spectroscopy |
title_short | Detection and Classification of Measurement Errors in Bioimpedance Spectroscopy |
title_sort | detection and classification of measurement errors in bioimpedance spectroscopy |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4928898/ https://www.ncbi.nlm.nih.gov/pubmed/27362862 http://dx.doi.org/10.1371/journal.pone.0156522 |
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