07-31, 13:50–14:00 (UTC), Green Track
The goal of this presentation will be to illustrate how the design
of equivalent electrical circuits for the analysis of electrochemical
(e.g. biological) systems can be optimized with an approach based
on evolutionary algorithms.
Electrochemical impedance spectroscopy (EIS) is the study of a
sample by measuring its resistance to alternating electrical currents (termed
impedance) over a range of frequencies. The data arising from these experiments
is commonly analysed by fitting an equivalent electrical circuit (EIC),
which consists of resistors, capacitors and possibly other electrical elements.
When considering well defined basic electrical systems, the configuration of
these circuits is relatively straightforward. More complex systems, such as
those arising in biology, where reasoning about the appropriate configurations
of the circuits becomes more challenging and subjective, can benefit from an
algorithmic approach.
An efficient Julia equivalent electrical circuit modelling and fitting module
that was implemented will be discussed. Next the evolutionary algorithms
used to build optimal circuit designs based on the biological measurement
data and fit the circuit-element parameters of the design, will be explained
along with some performance metrics.
Bio-engineer and PhD student at the bio-science engineering department of mathematical modeling and data-analysis of Ghent university.