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Authors: Thomas Schmid 1 ; Dorothee Günzel 2 and Martin Bogdan 1

Affiliations: 1 Universität Leipzig, Germany ; 2 Institute of Clinical Physiology, Germany

ISBN: 978-989-758-011-6

Keyword(s): Impedance Spectroscopy, Epithelia, HT-29/B6, IPEC-J2, Machine Learning, Feature Selection, Decision Trees, Artificial Neural Networks, Random Forests.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computer Vision, Visualization and Computer Graphics ; Data Manipulation ; Electromagnetic Fields in Biology and Medicine ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Medical Image Detection, Acquisition, Analysis and Processing ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Physiological Processes and Bio-Signal Modeling, Non-Linear Dynamics ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: In epithelial physiology, it is common to use an equivalent electric circuit with two resistor-capacitor (RC) subcircuits in series as a model for the electrical behavior of body cells. The relation between these two subcircuits can be quantified by a quotient of their time constants t. While this quotient is a direct indicator of the shape of impedance spectra, its value cannot be determined directly. Here, we suggest a machine learning-based approach to predict the t quotient from impedance spectra. We perform systematic extraction of statistical features, algorithmic feature ranking and dimension reduction on model impedance spectra derived from tissue-equivalent electric circuits. Our results demonstrate that this quotient can be predicted reliably enough from implicit features to discriminate semicircular against non-semicircular impedance spectra.

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Paper citation in several formats:
Schmid T., Günzel D. and Bogdan M. (2014). Automated Quantification of the Relation between Resistor-capacitor Subcircuits from an Impedance Spectrum.In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2014) ISBN 978-989-758-011-6, pages 141-148. DOI: 10.5220/0004746501410148

@conference{biosignals14,
author={Thomas Schmid and Dorothee Günzel and Martin Bogdan},
title={Automated Quantification of the Relation between Resistor-capacitor Subcircuits from an Impedance Spectrum},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2014)},
year={2014},
pages={141-148},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004746501410148},
isbn={978-989-758-011-6},
}

TY - CONF

JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2014)
TI - Automated Quantification of the Relation between Resistor-capacitor Subcircuits from an Impedance Spectrum
SN - 978-989-758-011-6
AU - Schmid T.
AU - Günzel D.
AU - Bogdan M.
PY - 2014
SP - 141
EP - 148
DO - 10.5220/0004746501410148

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