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A Single-Pair Antenna Microwave Medical Detection System Based on Unsupervised Feature Learning

  • Yizhi WuEmail author
  • Bingshuai LiuEmail author
  • Mingda Zhu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11280)

Abstract

The microwave medical detection method is an emerging non-invasive technology, which starts showing great potential in microwave biomedical applications. However, the practical application of it still faces challenges such as the detection equipment is complicated and difficult to control, and various interferences in the empirical situation. The difference between the microwave signal of healthy organs and that of the patients with stroke is sometimes too subtle to be detected when there are various noises within the detecting environment. This paper designed a single-pair antenna microwave medical detection system based on unsupervised feature learning for stroke detection. The system uses unsupervised feature learning, principal component analysis (PCA), to extract features, and then uses support vector machine (SVM) to classify whether there is a stroke. The use of a single-pair antenna greatly reduces the dimensionality of the sample features and also eliminates the interference between antenna arrays. This paper also optimized the detection position of the single-pair antenna. The performance of the detection system was verified by simulation and experiment. The results show that in the case of random interference, the detection system will also achieve better results, and when the antenna is placed in the left and right of the brain, the best performance will be achieved.

Keywords

Microwave medical detection Stroke Single-pair antenna PCA SVM 

References

  1. 1.
    Fhager, A., Candefjord, S., Elam, M., Persson, M.: Microwave diagnostics ahead: saving time and the lives of trauma and stroke patients. IEEE Microw. Mag. 19(3), 78–90 (2018)CrossRefGoogle Scholar
  2. 2.
    Gabriel, S., Lau, R.W., Gabriel, C.: The dielectric properties of biological tissues: II. Measurements in the frequency range 10 Hz to 20 GHz. Phys. Med. Biol. 41(11), 2251–2269 (1996)CrossRefGoogle Scholar
  3. 3.
    Guo, L., Abbosh, A.M.: Optimization-based confocal microwave imaging in medical applications. IEEE Trans. Antennas Propag. 63(8), 3531–3539 (2015)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Huang, J., Shao, X., Wechsler, H.: Face pose discrimination using support vector machines (SVM). In: Proceedings of the Fourteenth International Conference on Pattern Recognition, vol. 1, pp. 154–156 (1998)Google Scholar
  5. 5.
    Li, Y., Porter, E., Santorelli, A., Popovi, M., Coates, M.: Microwave breast cancer detection via cost-sensitive ensemble classifiers: phantom and patient investigation. Biomed. Signal Process. Control. 31, 366–376 (2017)CrossRefGoogle Scholar
  6. 6.
    Mavroforakis, M.E., Theodoridis, S.: Support vector machine (SVM) classification through geometry. In: Proceedings of EUSIPCO (2010)Google Scholar
  7. 7.
    Mobashsher, A.T., Abbosh, A.M.: Artificial human phantoms: human proxy in testing microwave apparatuses that have electromagnetic interaction with the human body. IEEE Microw. Mag. 16(6), 42–62 (2015)CrossRefGoogle Scholar
  8. 8.
    Mohammed, B.J., Abbosh, A.M., Ireland, D.: Stroke detection based on variations in reflection coefficients of wideband antennas. In: Antennas and Propagation Society International Symposium, pp. 1–2 (2012)Google Scholar
  9. 9.
    Mohammed, B.J., Abbosh, A.M., Mustafa, S., Ireland, D.: Microwave system for head imaging. IEEE Trans. Instrum. Meas. 63(1), 117–123 (2013)CrossRefGoogle Scholar
  10. 10.
    Moore, B.: Principal component analysis in linear systems: controllability, observability, and model reduction. IEEE Trans. Autom. Control. 26(1), 17–32 (2003)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Mustafa, S., Abbosh, A., Henin, B., Ireland, D.: Brain stroke detection using continuous wavelets transform matching filters. In: Biomedical Engineering Conference, pp. 194–197 (2012)Google Scholar
  12. 12.
    Persson, M., Fhager, A., Trefn, H.D., Yu, Y.: Microwave-based stroke diagnosis making global prehospital thrombolytic treatment possible. IEEE Trans. Biomed. Eng. 61(11), 2806–2817 (2014)CrossRefGoogle Scholar
  13. 13.
    Peyman, A., Rezazadeh, A.A., Gabriel, C.: Changes in the dielectric properties of rat tissue as a function of age atmicrowave frequencies. Phys. Med. Biol. 46(6), 1617–29 (2001)CrossRefGoogle Scholar
  14. 14.
    Semenov, S.Y., Corfield, D.R.: Microwave tomography for brain imaging: feasibility assessment for stroke detection. Int. J. Antennas Propag. 2008(4), 264–276 (2008)Google Scholar
  15. 15.
    Yu, Y., Mckelvey, T.: A unified subspace classification framework developed for diagnostic system using microwave signal. In: Signal Processing Conference, pp. 1–5 (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.College of Information Science and TechnologyDonghua UniversityShanghaiChina
  2. 2.Department of Electrical Engineering and Computer ScienceSyracuse UniversitySyracuseUSA

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