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Mike Nonte
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Apply voltage or current with known
frequency and amplitude
Record current or voltage response
Use phase shift and change in magnitude to
determine complex impedance
Sweep through a range of frequencies to
produce a nyquist plot
[1]
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Data Set
◦ EIS recordings from 106 freshly excised breast
tissue samples
◦ Each sample belongs to one of six tissue types:
1.
2.
3.
4.
5.
6.
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Carcinoma
Fibro-adenoma
Mastopathy
Glandular
Connective
Adipose
Problem: use pattern classification
techniques to reliably determine tissue type
from EIS recordings
[2]
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Replace ELMs with MLPs and compare
computation speed and accuracy
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Publically available data has nine features
already extracted:
I0: Impedance at zero frequency
PA500: Phase angle at 500kHz
HFS: High-frequency slope of phase angle
DA: Impedance distance between spectral ends
AREA: Area under the nyquist plot
A/DA: AREA normalized by DA
MAX OP: Maximum of the spectrum
DR: Distance between I0 and real component of the
maximum frequency point
◦ P: Length of the spectral curve
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Previous work [2] uses mutual information to
rank attribute strength then tests different
feature vector dimensions to determine which
yields best results
Only 9 feature attributes, so an exhaustive
subset selection approach is slow but
possible
◦ Randomly split data into equally sized testing and
training sets
◦ Train a single ELM and measure classification rate
with each possible set of attributes
◦ Determine optimal feature vector
0.65
0.6
0.55
Classification Rate
0.5
0.45
0.4
0.35
0.3
IO P DA DR AREA
PA500 P
P
IO PA500
0.25
0.2
0
100
200
300
400
500
600
# Neurons in Hidden Layer
700
800
900
1000
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Short-term
◦ Apply ELM outputs to multi-class SVM
◦ Replace ELMs with MLPs and compare speed and
accuracy of classification
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Long-term
◦ Obtain larger data set to ensure generalization of
results
◦ Examine new attributes that may be more useful in
determining a physiological basis for observed
impedance properties
[1] Williams, J. C., Hippensteel, J. A., Dilgen, J.,
Shain, W., & Kipke, D. R. (2007). Complex
impedance spectroscopy for monitoring tissue
responses to inserted neural implants. Journal
of neural engineering, 4(4), 410.
[2] Daliri, M. R. (2013). Combining extreme
learning machines using support vector
machines for breast tissue
classification. Computer methods in
biomechanics and biomedical engineering,
(ahead-of-print), 1-7.

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