Bioimpedance spectroscopy (BIS) uses multifrequency impedance measurements of biological tissues to estimate clinically and experimentally relevant parameters. This article reviews the steps involved in measurement, data processing, and applications of BIS data, with an emphasis on managing data quality and sources of errors. Based on a description of error sources, caused by measurement configuration, hardware, and modeling, we describe BIS data denoising. Two classes of modeling, explanatory and descriptive, can be used to reduce data dimensionality to a set of parameters or features. Explanatory models consider the electrical properties of samples and involve fitting data to simplified equivalent electrical circuits. Descriptive models involve reduction of the data to a set of eigenvectors/values which can be studied independently of any assumed electrical characteristics of the sample. Techniques described include fitting and decomposition methods for extraction of explanatory and descriptive model parameters, respectively. Denoising techniques discussed include adjusting measurement configuration, corrective algorithms for removal of artifacts, and use of supervised machine learning for identification of features characteristic of noisy impedance spectra. The article concludes with a discussion of the use of classifiers for labeling BIS data in a range of applications including discrimination of healthy versus pathological tissues.

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Department of Systems and Computer Engineering

Caytak, H. (Herschel), Boyle, A. (Alistair), Adler, A, & Bolic, M. (Miodrag). (2018). Bioimpedance spectroscopy processing and applications. In Encyclopedia of Biomedical Engineering (pp. 265–279). doi:10.1016/B978-0-12-801238-3.10884-0