Epilepsy, epileptic seizures, PNES, unexplained seizures Epilepsy
Conditions
Interventions
The intervention consists of the visual stimulation during the EEG recording. 
This consists of three different modalities: a stroboscopic flash lamp with 
flashes between 1 and 60 Hz, as standard in the clinic
LED goggles with chirp 
and a screen with alternating red-blue stimulation with varying 
modulation depth and vertical stripes, with and without color.
This will last about half an hour. This is no therapeutic intervention, only 
the direct response to the stimulation in the EEG will be evauated and 
compared.
Sponsors
Stichting Epilepsie Instellingen Nederland (SEIN)
Eligibility
Age
18 Years to 99 Years
Inclusion criteria
Inclusion criteria: Controls must: - Be over 18 years - Have sufficient proficiency in Dutch or English EMU patients must: - Be over 18 years - Have sufficient proficiency in Dutch or English - Meet the ILAE criteria for epilepsy, diagnosed by trained neurologists.
Exclusion criteria
Exclusion criteria: Healthy controls must not - Have previously been diagnosed with epilepsy (e.g. childhood epilepsy) - Have previously been diagnosed with PNES - Have a 1st-degree family member with epilepsy - Have a neurological (intracranial/CNS) condition that likely influences cortical excitability - Have a neurological (intracranial/CNS) condition that is severe enough to take neuroactive medication for EMU participants must not - Have no diagnosis of epilepsy - Have any neurological (intracranial/CNS) condition other than epilepsy or PNES that likely influences cortical excitability - Have any neurological (intracranial/CNS) condition other than epilepsy that is severe enough to take neuroactive medication for All participants must not - Have any condition which prevents them from sitting still for 30 minutes - Have any condition which prevents them from concentrating for 30 minutes - Have any history of problems or conditions involving eyesight (excluding pre-scription lenses or glasses) - Have any psychiatric disorder - Be unable to sign their own consent form (no legal representative) - Be pregnant
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| The classification performance of the machine learning model using biomarkers analysed in EEG-responses during various types of visual stimulation to distinguish between people with epilepsy and healthy participants. This will be assessed based on sensitivity, specificity, accuracy, and the area under the ROC curve (AUROC). | — |
Secondary
| Measure | Time frame |
|---|---|
| The classification performance of a machine learning model using these biomarkers to distinguish focal epilepsy from generalised epilepsy will be assessed based on sensitivity, specificity, accuracy, and the area under the ROC curve (AUROC). The classification performance of a machine learning model using these biomarkers to distinguish PNES from healthy individuals will be assessed based on sensitivity, specificity, accuracy, and the area under the ROC curve (AUROC). Identification of the most important EEG biomarkers contributing to model classification of people with or without epilepsy will be done by extracting them from the model. | — |
Countries
Netherlands
Contacts
Public ContactR.M. Helling
Stichting Epilepsie Instellingen Nederland (SEIN)
Outcome results
None listed