Diagnosis of active epilepsy in resource-poor setting

Reducing the burden of epilepsy

Prediction models based on EEG characteristics

life sciences and ehealth

Diagnosis of active epilepsy in resource-poor setting

Diagnosis of active epilepsy in resource-poor setting

Principal Investigator
Dr. Wim M. Otte
UMC Utrecht

Prediction models based on EEG characteristics

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.

Image: Sadasiv Swain - Distributing anti-epileptic drugs in Jalda, India https://creativecommons.org/licenses/by/2.0/

eScience Research Engineer Dr. Vincent van Hees

Vincent works on the project Compressing the sky into a large collection of statistical models.

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