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  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  Replace ELMs with MLPs and compare computation speed and accuracy 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  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 0.65 0.6 0.55 Classification Rate 0.5 0.45 0.4 0.35 0.3 IO P DA DR AREA PA500 P P IO PA500 0.25 0.2 0 100 200 300 400 500 600 # Neurons in Hidden Layer 700 800 900 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  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.  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.