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Differentiation of brain stroke type by using microwave-based machine learning classification

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Olympia Karadima, Raquel C. Conceicao, Panagiotis Kosmas

Original languageEnglish
Title of host publication2021 International Conference on Electromagnetics in Advanced Applications, ICEAA 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages185
Number of pages1
ISBN (Electronic)9781665413862
DOIs
Published9 Aug 2021
Event22nd International Conference on Electromagnetics in Advanced Applications, ICEAA 2021 - Honolulu, United States
Duration: 9 Aug 202113 Aug 2021

Publication series

Name2021 International Conference on Electromagnetics in Advanced Applications, ICEAA 2021

Conference

Conference22nd International Conference on Electromagnetics in Advanced Applications, ICEAA 2021
Country/TerritoryUnited States
CityHonolulu
Period9/08/202113/08/2021

Bibliographical note

Publisher Copyright: © 2021 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

King's Authors

Abstract

Brain stroke is an emergency condition that is caused either by a blocked or a burst vessel (ischemic or hemorrhagic stroke, respectively), resulting into abnormal blood supply into the affected area, with severe and sometimes deadly consequences. Early diagnosis of the stroke is vital, as the time that passes from the offset of the symptoms is strictly correlated with the survival of the patient and the treatment success. Concurrently, it is also essential to successfully identify the type of the stroke as treating a hemorrhagic stroke (h-stroke) as an ischemic (i-stroke) stroke could be lethal for the patient [1]. Therefore, there is an increased need for a portable and low-cost diagnostic method that will detect and differentiate the type of the brain stroke as early as possible.

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