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2016-12-13
Subspace Pattern Recognition Method for Brain Stroke Detection
By
Progress In Electromagnetics Research Letters, Vol. 64, 119-126, 2016
Abstract
Brain stroke is a serious disease and one of the major causes of death. Stroke detection based on the frequently studied microwave imaging method is computation-intensive and not always reliable. This paper presents a stroke-detection scheme based on subspace classification technique. Specifically, the stroke is detected and located using the intersection of the positive antenna lines, i.e. connecting the transmitter and receiver. The numerical results show that the proposed method can detect and locate blood clots efficiently.
Citation
Yizhi Wu, Xieyun Xu, and Ming-Da Zhu, "Subspace Pattern Recognition Method for Brain Stroke Detection," Progress In Electromagnetics Research Letters, Vol. 64, 119-126, 2016.
doi:10.2528/PIERL16091001
References

1. Irishina, N. and A. Torrente, "Brain stroke detection by microwaves using prior information from clinical databases," Abstract and Applied Analysis, Vol. 2013, Article ID 412638, 8 pages, 2013.

2. AlShehri, S. A. and S. Khatun, "UWB imaging for breast cancer detection using neural network," Progress In Electromagnetics Research C, Vol. 7, 79-93, 2009.
doi:10.2528/PIERC09031202

3. Shao, W. and B. Zhou, "UWB microwave imaging for breast tumor detection in inhomogeneous tissue," Proceedings of the 2005 IEEE Engineering in Medicine and Biology, 27th Annual Conference, 1496-1499, Shanghai, China, 2005.
doi:10.1109/IEMBS.2005.1616715

4. Sill, J. M. and E. C. Fear, "Tissue sensing adaptive radar for breast cancer detection-experimental investigation of simple tumor models," IEEE Transactions on Microwave Theory and Techniques, Vol. 53, No. 11, 3312-3319, Nov. 2005.
doi:10.1109/TMTT.2005.857330

5. Ireland, D. and M. E. Bialkowski, "Microwave head imaging for stroke detection," Progress In Electromagnetics Research M, Vol. 21, 163-175, 2011.
doi:10.2528/PIERM11082907

6. Mobashsher, T., A. M. Abbosh, and Y. Wang, "Microwave system to detect traumatic brain injuries using compact unidirectional antenna and wideband transceiver with verification on realistic head phantom," IEEE Transactions on Microwave Theory and Techniques, Vol. 62, No. 9, 1826-1836, Sep. 2014.
doi:10.1109/TMTT.2014.2342669

7. Donelli, M. and A. Massa, "Computational approach based on a particle swarm optimizer for microwave imaging of two-dimensional dielectric scatterers," IEEE Transactions on Microwave Theory And Techniques, Vol. 53, No. 5, 1761-1776, May 2005.
doi:10.1109/TMTT.2005.847068

8. Persson, M., A. Fhager, H. Trefna, Y. Yu, T. McKelvey, G. Pegenius, et al. "Microwave-based stroke diagnosis making global pre-hospital thrombolytic treatment possible," IEEE Transactions on Biomedical Engineering, Vol. 61, 2806-2817, Nov. 2014.

9. Mohammed, B., A. Abbosh, and D. Ireland, "Stroke detection based on variations in reflection coefficients of wideband antennas," Antennas and Propagation Society International Symposium (APSURSI), IEEE, 2012.

10. Mustafa, S., A. Abbosh, B. Henin, and D. Ireland, "Brain stroke detection using continuous wavelets transform matching filters," Biomedical Engineering Conference (CIBEC), 2012 Cairo International, 194-197, 2012.
doi:10.1109/CIBEC.2012.6473328

11. Khorshidi, M. A., T. McKelvey, M. Persson, and H. D. Trefna, "Classification of microwave scattering data based on a subspace distance with application to detection of bleeding stroke," 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 301-304, 2009.

12. Watanabe, S. and N. Pakvasa, "Subspace method in pattern recognition," Proc. Int. Joint Conf. Pattern Recognition, 25-32, 1973.

13. Yu, Y. and T. McKelvey, "Unified subspace classification framework developed for diagnostic system using microwave signal," 231st Eur. Signal Process. Conf., Marrakech, Marocco, Sep. 2013.

14. Yu, Y., "Classification of high dimensional signals with small training sample size with applications towards microwave based detection systems," Lic. Thesis, Chalmers Univ. Technol., Goteborg, Sweden, 2013.

15. Yu, Y. and T. McKelvey, "A unified subspace classification framework developed for dlagnostic system using microwave signal," European Signal Processing Conference, 2219-5491, 2013.

16. Golub, G. H. and C. F. Van Loan, Matrix Computations, 2nd Ed., Johns Hopkins Univ. Press, 1989.

17. Ireland, D. and A. Abbosh, "Modeling human head at microwave frequencies using optimized debye models and FDTD method," IEEE Transactions on Antennas And Propagation, Vol. 61, No. 4, 2352-2355, Apr. 2013.
doi:10.1109/TAP.2013.2242037

18. Ireland, D. and M. E. Bialkowski, "Microwave head imaging for stroke detection," Progress In Electromagnetics Research M, Vol. 21, 163-175, 2011.
doi:10.2528/PIERM11082907

19. Ireland, D. and M. E. Bialkowski, "Feasibility study on microwave stroke detection using a realistic phantom and the FDTD method," Proc. Asia-Pacific Microwave Conf., 1-4, 2010.

20. Zubal, G., C. R. Harrell, E. O. Smith, Z. Rattner, G. Gindi, and P. B. Hoffer, "Computerized three-dimensional segmented human anatomy," Medical physics, Vol. 21, 299-302, 1994.
doi:10.1118/1.597290

21. Gabriel, S., R. Lau, and C. Gabriel, "The dielectric properties of biological tissues: II. Measurements in the frequency range 10 Hz to 20 GHz," Physics in Medicine and Biology, Vol. 41, 2251, 1996.
doi:10.1088/0031-9155/41/11/002

22. Shen, W., "The principle of vector network analyzer,", Vol. 5, 018, 2001.