Brain stroke localization by using microwave-based signal classification

Wu, Yizhi, Zhu, Mingda, Li, Danmei, Zhang, Youtao and Wang, Yifan (2016). Brain stroke localization by using microwave-based signal classification. In: Proceedings of the 2016 18th International Conference on Electromagnetics in Advanced Applications, ICEAA 2016. 18th International Conference on Electromagnetics in Advanced Applications, ICEAA 2016, Cairns, QLD, Australia, (828-831). 19 - 23 September 2016. doi:10.1109/ICEAA.2016.7731527


Author Wu, Yizhi
Zhu, Mingda
Li, Danmei
Zhang, Youtao
Wang, Yifan
Title of paper Brain stroke localization by using microwave-based signal classification
Conference name 18th International Conference on Electromagnetics in Advanced Applications, ICEAA 2016
Conference location Cairns, QLD, Australia
Conference dates 19 - 23 September 2016
Convener IEEE
Proceedings title Proceedings of the 2016 18th International Conference on Electromagnetics in Advanced Applications, ICEAA 2016
Journal name Proceedings of the 2016 18th International Conference on Electromagnetics in Advanced Applications, ICEAA 2016
Place of Publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Publication Year 2016
Sub-type Fully published paper
DOI 10.1109/ICEAA.2016.7731527
Open Access Status Not yet assessed
ISBN 9781467398114
9781467398107
9781467398121
Start page 828
End page 831
Total pages 4
Collection year 2018
Language eng
Abstract/Summary This research presents a new methodology of developing the brain stroke localization algorithm by using the machine-learning based pattern recognition approach for foreign object classification. Due to the new algorithm requires less bandwidth from the system hardware level, the proposed method would be helpful to reduce the challenges of designing a high-performance UWB antenna array required from most microwave stroke detection system nowadays. The presented algorithm employs the interconnection point of two lines that intersect the stroke region inside a brain and evaluates the properties of the stroke by using Support Vector Machines (SVM) classification method; the SVM classifier is trained by the data set acquired from EM simulations as well as the optimized coefficients from PSO algorithm. The proposed methodology of brain stroke classification has been verified by simulated data. The performance and effectiveness demonstrated it's a promising algorithm to be installed in the next-generation microwave brain stroke detection system.
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
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