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