Mike 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

[1]

  ◦ ◦ Data Set EIS recordings from 106 freshly excised breast tissue samples Each sample belongs to one of six tissue types: 1.

Carcinoma 2.

3.

4.

5.

6.

Fibro-adenoma Mastopathy Glandular Connective Adipose Problem: use pattern classification techniques to reliably determine tissue type from EIS recordings

 Replace ELMs with MLPs and compare computation speed and accuracy [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

  ◦ ◦ ◦ 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

0.4

0.35

0.3

0.25

0.2

0.65

0.6

0.55

0.5

0.45

0 100 200 300 400 500 600 # Neurons in Hidden Layer 700 IO P DA DR AREA PA500 P P IO PA500 800 900 1000

  ◦ ◦ ◦ ◦ 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

[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

[2] Daliri, M. R. (2013). Combining extreme learning machines using support vector machines for breast tissue classification.

,

4

(4), 410.

Computer methods in biomechanics and biomedical engineering

, (ahead-of-print), 1-7.