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Machine Learning Methods for EEG-based Epileptic Seizure Detection
KTH, School of Electrical Engineering and Computer Science (EECS).
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Maskininlärningsmetoder för EEG-baserad detektion av epileptiska anfall (Swedish)
Abstract [en]

Epilepsy is one of the most common neurological diseases that affects millions of persons all over the world. The disease has always been of great importance in the biomedical field, due to the health risks it causes. It is characterized by recurrent, unprovoked seizures and can be assessed by the electroencephalogram (EEG). EEG measures the electrical activity in the brain, and one important aspect of the epilepsy research includes analyzing the EEG data in order to detect epileptic seizures in early stages. A lot of work has been done on patient-specific classifiers, but building patient-independent models is more difficult. This thesis focuses on the cross-patient view as it is more complicated due to EEG variability between different subjects. A comparative analysis of pattern recognition algorithms employed for EEG-based epileptic seizure identification was done. The algorithms compared was the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). Our study shows that the two methods perform similar, although KNN achieved a slightly higher accuracy during certain conditions.

Abstract [sv]

Epilepsi är en av de vanligaste neurologiska sjukdomarna, vilken påverkar miljontals av människor över hela världen. Sjukdomen har alltid varit relevant inom det biomedicinska området på grund av hälsoriskerna den orsakar. Epilepsi karakteriseras av upprepade, oprovocerade anfall och kan fastställas med hjälp av elektroencefalografi (EEG). EEG mäter den elektriska aktiviteten i hjärnan, och en viktig aspekt inom epilepsiforskning inkluderar analys av EEG-data för att kunna detektera epileptiska anfall i ett tidigt skede. Mycket arbete har hittills gjorts på patient-specifika klassificeringsmetoder, medan det är svårare att bygga patient-oberoende modeller. Denna studie fokuserar på patient-oberoende klassificering eftersom den är mer komplicerad på grund av hur EEG-data skiljer sig mellan olika individer. En jämförelse av maskinlärningsmetoder för EEG-baserad detektion av epileptiska anfall utfördes. Algoritmerna som jämfördes var Support Vector Machine (SVM) och K-Nearest Neighbor (KNN). Vår studie visar att båda metoderna gav liknande resultat, dock uppnådde KNN en något högre noggranhet under vissa omständigheter.

Place, publisher, year, edition, pages
2019. , p. 28
Series
TRITA-EECS-EX ; 2019:346
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-259638OAI: oai:DiVA.org:kth-259638DiVA, id: diva2:1352708
Supervisors
Examiners
Available from: 2019-09-24 Created: 2019-09-19 Last updated: 2019-09-24Bibliographically approved

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