The present method consists of using impedance.py python library for fitting the circuit directly to the lab data. Accuracy metrics are yet to be improvised by adjusting the circuit model.

1. Online python library
for impedance processing
Rishabh Garg
Birla Institute of Technology & Science
Pilani | K.K. Birla Goa Campus

2. RISHABH GARG
BITS - PILANI | GOA
Design Project
Online
PYTHON LIBRARY
for impedance processing

3. RISHABH GARG
BITS - PILANI | GOA
Libraries
• impedance.py is an excellent library for managing the EIS data.
• impedance.py offers easy-to-use functions for importing data
from BioLogic, CH Instruments, Gamry, PARSTAT, VersaStudio,
and ZView files.
• In order to express the characterization of surfaces, layers or
membranes, EIS is analyzed using an equivalent circuit which is
used to curve fit the experimental data. This curve fitting is
efficiently done by impedance.py library. Columns: frequency,
real part of impedance, imaginary part of impedance.

4. RISHABH GARG
BITS - PILANI | GOA
Libraries
• Another curve fitting library for generating equivalent circuit:
https://github.com/Samuel-Buteau/EISFitting
• To train this library, the normalized EIS was used as the input data and the
corresponding equivalent circuit as the output data. SVM model is used to
deal with EIS and provide classification suggestions for EIS equivalent
circuit model.
• The time needed for the electrical parameters Rs and Cs to deviate from
their baseline value is referred as Detect Time (DT) and is the parameter
used to estimate the initial unknown bacterial concentration C0.

5. RISHABH GARG
BITS - PILANI | GOA
ML Models
• Various different equivalent circuits can fit the impedance data. Hence
circuit simplification needs to be done.
• Process of estimation of bacterial concentration from the equivalent circuit
also requires some effort.
• XGBoost and a support vector regression (SVR) machine learning models
can be used to establish a quantitative relationship between multiple
impedance parameters and the bacterial concentration.
• SVM model to analyze the EIS data without equivalent circuit fitting: 114
SVM models with four different kernels (polynomial, sigmoidal, linear, and
radial basis function) were compared to find the optimized ML model.

6. RISHABH GARG
BITS - PILANI | GOA
ML Models
• 80% of 54 EIS data were randomly selected as training data set and the
other 20% data were attributed to the testing data set. The SVM with radial
base function kernel was demonstrated to have the optimal performance for
classifying the training data set with the accuracy of 98%.
• A machine learning model, using principle component analysis and support
vector regression, can be trained to automatically establish a quantitative
relationship between multiple impedimetric parameters and bacterial
concentrations.
• A common approach also consists of using ML Models to identify the
relationship between the circuit parameters and bacterial concentration
rather than plotting Nyquist and Bode plots.

7. RISHABH GARG
BITS - PILANI | GOA
Input Data
• In DI water suspensions, impedance at 1 kHz decreased with the increasing
cell concentrations in the suspensions.
• Sampling rate: 10 measurements per decade of frequency

8. RISHABH GARG
BITS - PILANI | GOA
Input Data

9. RISHABH GARG
BITS - PILANI | GOA
Design Project
Thank you !