Classification of Breast Tissue Using Electrical Impedance Spectroscopy

Classification of Breast Tissue Using Electrical Impedance Spectroscopy Mike Nonte

Electrical Impedance Spectroscopy � 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

EIS for Tissue Classification [1]

Breast Tissue Classification � 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. � Carcinoma Fibro-adenoma Mastopathy Glandular Connective Adipose Problem: use pattern classification techniques to reliably determine tissue type from EIS recordings

Proposed Method [2] � Replace ELMs with MLPs and compare computation speed and accuracy

Feature Extraction � Publically available data has nine features already extracted: I 0: Impedance at zero frequency PA 500: Phase angle at 500 k. Hz 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 I 0 and real component of the maximum frequency point ◦ P: Length of the spectral curve ◦ ◦ ◦ ◦

Feature Selection � 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

Preliminary Data

Future Work � 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

Questions

References [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.