mike nonte. apply voltage or current with known frequency and amplitude record current or voltage...
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Classification of Breast Tissue Using Electrical
Impedance SpectroscopyMike Nonte
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
Electrical Impedance Spectroscopy
EIS for Tissue Classification
[1]
Data Set◦ EIS recordings from 106 freshly excised breast
tissue samples◦ Each sample belongs to one of six tissue types:
1. Carcinoma2. Fibro-adenoma3. Mastopathy4. Glandular5. Connective6. Adipose
Problem: use pattern classification techniques to reliably determine tissue type from EIS recordings
Breast Tissue Classification
Replace ELMs with MLPs and compare computation speed and accuracy
Proposed Method
[2]
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
Feature Extraction
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
Feature Selection
0 100 200 300 400 500 600 700 800 900 1000
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65
# Neurons in Hidden Layer
Cla
ssifi
catio
n R
ate
IO P DA DR AREA
PA500 PP
IO PA500
Preliminary Data
Short-term◦ Apply ELM outputs to multi-class SVM◦ Replace ELMs with MLPs and compare speed and
accuracy of classification 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
Future Work
Questions
[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.
References