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 ID212745
Article CategoryArticle
AuthorsZhu, Guohun, Bialkowski, Alina, Guo, Lei, Mohammed, Beadaa and Abbosh, Amin
Journal TitleIEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology
Journal Citation5 (1), pp. 46-53
Number of Pages8
YearMar 2021
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
ISSN2469-7249
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...

KeywordsComplex networks; Electromagnetic measurements; Microwave imaging; Mutual information; Support vector machines
ANZSRC Field of Research 2020400399. Biomedical engineering not elsewhere classified
460207. Modelling and simulation
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Byline AffiliationsUniversity of Queensland
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