liu.seSearch for publications in DiVA
Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Localization of Stroke Using Microwave Technology and Inner product Subspace Classifier
Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska högskolan.
2014 (engelsk)Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
Abstract [en]

Stroke or “brain attack” occurs when a blood clot carried by the blood vessels from other part of the body blocks the cerebral artery in the brain or when a blood vessel breaks and interrupts the blood flow to parts of the brain. Depending on which part of the brain is being damaged functional abilities controlled by that region of the brain is lost. By interpreting the patient’s symptoms it is possible to make a coarse estimate of the location of the stroke, e.g. if it is on the left or right hemisphere of the brain. The aim of this study was to evaluate if microwave technology can be used to estimate the location of haemorrhagic stroke.

In the first part of the thesis, CT images of the patients for whom the microwave measurement are taken is analysed and are used as a reference to know the location of bleeding in the brain. The X, Y and Z coordinates are calculated from the target slice (where the bleeding is more prominent). Based on the bleeding coordinated the datasets are divided into classes. Under supervised learning method the ISC algorithm is trained to classify stroke in the left and right hemispheres; stroke in the anterior and posterior part of the brain and the stroke in the inferior and superior region of the brain. The second part of the thesis is to analyse the classification result in order to identify the patients that were being misclassified.

The classification results to classify the location of bleeding were promising with a high sensitivity and specificity that are indicated by the area under the ROC curve (AUC). AUC of 0.86 was obtained for bleedings in the left and right brain and an AUC of 0.94 was obtained for bleeding in the inferior and superior brain. The main constraint was the small size of the dataset and few availability of dataset with bleeding in the front brain that leads to imbalance between classes. After analysis it was found that bleedings that were close to the skull and few small bleedings that are deep inside the brain are being misclassified. Many factors can be responsible for misclassification like the antenna position, head size, amount of hair etc.

The overall results indicate that SDD using ISC algorithm has high potential to distinguish bleedings in different locations. It is expected that the results will be more stable with increased patient dataset for training.

sted, utgiver, år, opplag, sider
2014. , s. 59
Emneord [en]
microwaves, pattern recognition, classifier
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-111585ISRN: LiTH-IMT/MASTER-EX--14/030--SEOAI: oai:DiVA.org:liu-111585DiVA, id: diva2:758305
Fag / kurs
Master's Program Biomedical Engineering
Presentation
2014-09-03, IMT 1, Linköping University, Linköping, 13:15 (engelsk)
Veileder
Examiner
Tilgjengelig fra: 2014-10-27 Laget: 2014-10-27 Sist oppdatert: 2015-11-03bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Søk i DiVA

Av forfatter/redaktør
Prabahar, Jasila
Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric

urn-nbn
Totalt: 303 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf