Stroke Classification in Simulated Electromagnetic Imaging Using Graph Approaches

Article

Zhu, Guohun, Bialkowski, Alina, Guo, Lei, Mohammed, Beadaa and Abbosh, Amin. 2021. "Stroke Classification in Simulated Electromagnetic Imaging Using Graph Approaches ." IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology. 5 (1), pp. 46-53. https://doi.org/10.1109/JERM.2020.2995329

Article Title	Stroke Classification in Simulated Electromagnetic Imaging Using Graph Approaches
ERA Journal ID	212745
Article Category	Article
Authors	Zhu, Guohun, Bialkowski, Alina, Guo, Lei, Mohammed, Beadaa and Abbosh, Amin
Journal Title	IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology
Journal Citation	5 (1), pp. 46-53
Number of Pages	8
Year	Mar 2021
Publisher	IEEE (Institute of Electrical and Electronics Engineers)
Place of Publication	United States
ISSN	2469-7249
	2469-7257
Digital Object Identifier (DOI)	https://doi.org/10.1109/JERM.2020.2995329
Web Address (URL)	https://ieeexplore.ieee.org/document/9095216
Abstract	Identifying stroke subtypes from electromagnetic imaging systems is usually based on frequency domain using radar or tomography algorithms which is computationally expensive. This paper presents a novel graph degree mutual information (GDMI) approach to distinguish Intracranial Haemorrhage (ICH) from Ischemic Stroke (IS). A total of 50 ICH and 50 IS signals simulated using a 16-antenna electromagnetic head imaging system are analysed to evaluate GDMI. The data collected from each model consists of 256 reflected and received signal. Subsequently, noise is injected into the collected signals to generate three groups of signals with different signal-to-noise ratios (40 dB, 25 dB and 10 dB SNR), to emulate measurement noise and to test the algorithm's robustness. Each signal is converted into a graph to avoid the variable signal amplitudes. Then, the relationship between each pair of graph degrees is calculated by mutual information and forwarded to a support vector machine to identify str...
Keywords	Complex networks; Electromagnetic measurements; Microwave imaging; Mutual information; Support vector machines
ANZSRC Field of Research 2020	400399. Biomedical engineering not elsewhere classified
	460207. Modelling and simulation
Public Notes	Files associated with this item cannot be displayed due to copyright restrictions.
Byline Affiliations	University of Queensland

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