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Predictive detection of epileptic seizures in EEG for reactive care
Blekinge Institute of Technology, Faculty of Engineering, Department of Applied Signal Processing.
Blekinge Institute of Technology, Faculty of Engineering, Department of Applied Signal Processing.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

It is estimated that 65 million people worldwide have epilepsy, and many of them have uncontrollable seizures even with the use of medication. A seizure occurs when the normal electrical activity of the brain is interrupted by sudden and unusually intense bursts of electrical energy, and these bursts can be observed and detected by the use of an electroencephalograph (EEG) machine. This work presents an algorithm that monitors subtle changes in scalp EEG characteristics to predict seizures. The algorithm is built to calibrate itself to every specifc patient based on recorded data, and is computationally effcient enough for future on-line applications. The presented algorithm performs ICA-based artifact filtering and Lasso-based feature selection from a large array of statistical features. Classification is based on a neural network using Bayesian regularized backpropagation.The selected method was able to classify 4 second long preictal segments with an average sensitivity of 99.53% and an average specificity of 99.9% when tested on 15 different patients from the CHB-MIT database.

Place, publisher, year, edition, pages
2017. , 43 p.
Keyword [en]
epilepsy, neural network, independent component analysis
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:bth-15078OAI: oai:DiVA.org:bth-15078DiVA: diva2:1136921
Subject / course
ET2566 Master's Thesis (120 credits) in Electrical Engineering with emphasis on Signal processing
Educational program
ETASB Master of Science Programme in Electrical Engineering with emphasis on Signal Processing
Presentation
2017-06-14, J3423 Aristoteles, Valhallavagen 1, 37141 Karlskrona, Karlskrona, 15:16 (English)
Supervisors
Examiners
Available from: 2017-08-30 Created: 2017-08-29 Last updated: 2017-08-30Bibliographically approved

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BTH2017Homsi(3684 kB)28 downloads
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf