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Diagnosis of active epilepsy in resource-poor setting

Prediction models based on EEG characteristics

Image: Sadasiv Swain – Distributing anti-epileptic drugs in Jalda, India

Winner of the Young eScientist Award 2015

Most people with active epilepsy live in rural areas in low and middle income countries. Despite the availability of cheap and save drugs, more than 80% is not on treatment due to lack of required diagnosis. Development of diagnostic methods that are available in rural setting and that do not rely on medical specialists will greatly help to reduce this large treatment gap. Recently, there have been promising results in using computers to diagnose epilepsy based on standard electroencephalography (EEG) recordings.

This path-finding project will bridge the gap between electroencephalography prediction modeling and epilepsy diagnosis in rural areas of Sub-Saharan Africa, where the burden of epilepsy is highest. EEG will be acquired in community services with wireless, affordable consumer-grade headsets. Diagnostic EEG prediction models will be developed with machine learning and signal processing algorithms. Models will be incorporated in a telemedicine web-portal to aide future diagnoses and reduce the significant burden of epilepsy.

Participating organisations

Netherlands eScience Center
University Medical Center Utrecht

Team

VvH
Vincent van Hees
Senior eScience Research Engineer
Netherlands eScience Center
WO
Wim M. Otte
Principal investigator
University Medical Center Utrecht

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Related tools

EEG epilepsy diagnosis

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R package developed to extract features from multivariate time series from EEG data and feed them into a random forest classifier.

Updated 5 months ago
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