Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/23606
DC FieldValueLanguage
dc.contributor.authorKyriacou, Efthyvoulos C.-
dc.contributor.authorVogazianos, Paris-
dc.contributor.authorChristodoulou, Christodoulos I.-
dc.contributor.authorLoizou, Christos P.-
dc.contributor.authorPanayides, Andreas S.-
dc.contributor.authorPetroudi, Styliani H.-
dc.contributor.authorPattichis, Marios S.-
dc.contributor.authorPantziaris, Marios-
dc.contributor.authorNicolaides, Andrew N.-
dc.contributor.authorPattichis, Constantinos S.-
dc.date.accessioned2021-11-10T09:44:45Z-
dc.date.available2021-11-10T09:44:45Z-
dc.date.issued2015-
dc.identifier.citation37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015, 25-29 August, Milan, Italyen_US
dc.identifier.isbn978-1-4244-9271-8-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/23606-
dc.description.abstractNon-invasive ultrasound imaging of carotid plaques can provide information on the characteristics of the arterial wall including the size, morphology and texture of the atherosclerotic plaques. Several studies were carried out that demonstrated the usefulness of these feature sets for differentiating between asymptomatic and symptomatic plaques and their corresponding cerebrovascular risk stratification. The aim of this study was to develop predictive modelling for estimating the time period of a stroke event by determining the risk for short term (less or equal to three years) or long term (more than three years) events. Data from 108 patients that had a stroke event have been used. The information collected included clinical and ultrasound imaging data. The prediction was performed at base line where patients were still asymptomatic. Several image texture analysis and clinical features were used in order to create a classification model. The different features were statistically analyzed and we conclude that image texture analysis features extracted using Spatial Gray Level Dependencies method had the best statistical significance. Several predictive models were derived based on Binary Logistic Regression (BLR) and Support Vector Machines (SVM) modelling. The best results were obtained with the SVM modelling models with an average correct classifications score of 77±7% for differentiating between stroke event occurrences within 3 years versus more than 3 years. Further work is needed in investigating additional multiscale texture analysis features as well as more modelling techniques on more subjects.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.rights© IEEEen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectPredictive modelsen_US
dc.subjectSupport vector machinesen_US
dc.subjectFeature extractionen_US
dc.subjectMorphologyen_US
dc.subjectComputational modelingen_US
dc.subjectUltrasonic imagingen_US
dc.subjectAtherosclerosisen_US
dc.titlePrediction of the time period of stroke based on ultrasound image analysis of initially asymptomatic carotid plaquesen_US
dc.typeConference Papersen_US
dc.collaborationFrederick Universityen_US
dc.collaborationUniversity of Cyprusen_US
dc.collaborationIntercollegeen_US
dc.collaborationImperial College Londonen_US
dc.collaborationUniversity of New Mexicoen_US
dc.collaborationCyprus Institute of Neurology and Geneticsen_US
dc.collaborationCyprus Cardiovascular Disease and Educational Research Trusten_US
dc.subject.categoryMedical Engineeringen_US
dc.countryCyprusen_US
dc.countryUnited Kingdomen_US
dc.countryUnited Statesen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.relation.conferenceAnnual International Conference of the IEEE Engineering in Medicine and Biology Societyen_US
dc.identifier.doi10.1109/EMBC.2015.7318367en_US
dc.identifier.pmid26736267-
dc.identifier.scopus2-s2.0-84953262340-
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/84953262340-
cut.common.academicyear2014-2015en_US
dc.identifier.spage334en_US
dc.identifier.epage337en_US
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_c94f-
item.cerifentitytypePublications-
item.openairetypeconferenceObject-
crisitem.author.deptDepartment of Electrical Engineering, Computer Engineering and Informatics-
crisitem.author.deptDepartment of Electrical Engineering, Computer Engineering and Informatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-4589-519X-
crisitem.author.orcid0000-0003-1247-8573-
crisitem.author.parentorgFaculty of Engineering and Technology-
crisitem.author.parentorgFaculty of Engineering and Technology-
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation
CORE Recommender
Show simple item record

SCOPUSTM   
Citations 50

2
checked on Feb 1, 2024

Page view(s) 50

301
Last Week
0
Last month
38
checked on Mar 14, 2025

Google ScholarTM

Check

Altmetric


This item is licensed under a Creative Commons License Creative Commons