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The Window Size for Classification of Epileptic Seizures based on Analysis of EEG Patterns
KTH, School of Computer Science and Communication (CSC). (Group 38)
KTH, School of Computer Science and Communication (CSC). (Group 38)
2016 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Tidsfönster för Klassificering av Epilepsianfall baserat på Analys av EEG-mönster (Swedish)
Abstract [en]

Epilepsy affects nearly 1% of the world’s population and is a disorder that affects the central nervous system where the nerve cell activity in the brain becomes disrupted and causes seizure. Further study in this field can minimize the potential damage caused by individuals with epilepsy. This thesis investigated the the optimal window size for classification of EEG epochs as scientists today simply guess a window size and therefore might not get the best possible results. The method that was used was an existing software where the window size could be easily changed and investigated. The software used Support Vector Machines and calculated the probability for seizures. The results from this investigation showed more fluctuating probabilities at lower and higher window sizes, and more stable at 60 seconds to 180 seconds. In conclusion, the optimal window size can be argued to be around 90 seconds as it has the highest maximum, minimum, and average probability. 

Place, publisher, year, edition, pages
2016. , 33 p.
National Category
Computer Science
URN: urn:nbn:se:kth:diva-186439OAI: diva2:927318
Available from: 2016-05-12 Created: 2016-05-11 Last updated: 2016-05-12Bibliographically approved

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