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Difference between revisions of "Exploring Algorithms for Early Seizure Detection"

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[[Category:Digital]] [[Category:Human Intranet]] [[Category:Completed]] [[Category:Master Thesis]] [[Category:2019]]
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[[Category:Digital]] [[Category:Human Intranet]] [[Category:In progress]] [[Category:Master Thesis]] [[Category:2019]]
 
[[Category:User:Herschmi]]
 
[[Category:User:Herschmi]]
 
[[File:Non-EEG Seizure.jpg|thumb|300px]]
 
[[File:Non-EEG Seizure.jpg|thumb|300px]]

Revision as of 14:39, 29 October 2019

Non-EEG Seizure.jpg

Description

Epilepsy is a severe and prevalent chronic neurological disorder affecting 1–2% of the world’s population. One third of epilepsy patients continue to suffer from seizures despite best possible pharmacological treatment. For these patients with so-called drug-resistant epilepsy, various algorithms based on intracranial electroencephalography (iEEG) recording are proposed to detect the onset of seizures. Among them are algorithms based on brain-inspired hyperdimensional (HD) computing available at http://ieeg-swez.ethz.ch/. The main goal of this project is to enhance current HD algorithms, or propose new ones, to especially reduce the delay of seizure onset detection on long-term iEEG dataset, ultimately pushing towards early seizure detection.

Status: In progress

Anna Summerauer

Supervision: Michael Hersche, Abbas Rahimi


Prerequisites

  • Machine Learning
  • Python Programming
  • Cuda Programming


Character

40% Theory
60% Programming

Professor

Luca Benini

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