Classification of Breast Tissue Using Electrical Impedance Spectroscopy

1. Classification of Breast Tissue Using Electrical Impedance Spectroscopy Mike Nonte

2. 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

3. EIS for Tissue Classification [1]

4. Breast Tissue Classification • Data Set • EIS recordings from 106 freshly excised breast tissue samples • Each sample belongs to one of six tissue types: • Carcinoma • Fibro-adenoma • Mastopathy • Glandular • Connective • Adipose • Problem: use pattern classification techniques to reliably determine tissue type from EIS recordings

5. Proposed Method • Replace ELMs with MLPs and compare computation speed and accuracy [2]

6. Feature Extraction • 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

7. 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

8. Preliminary Data

9. 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

10. Questions

11. 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.