This study is a single center, random participant selection, data analyst is blinded to patient identifiers, controlled clinical trial. The proposed study is intended to establish safety and efficacy of quantifiable electrical biomarkers for migraine that can be used to confirm a diagnosis in people that have already been screened as positive for migraine using the gold standard participative criteria set out in the International Classification of Headache disorders-3 (ICHD-3) criteria. It is hypothesized that specific brain signals can be used to distinguish between migraine patients with and without aura from normal control and tension- type headache control participants by EEG enhanced with machine learning software.
Study Type
OBSERVATIONAL
Enrollment
20
Resting EEG, and Visual and Auditory Stimulation
Headache Sciences Incorporated Laboratory
Toronto, Ontario, Canada
RECRUITINGFeasibility of machine learning as applied to EEG to diagnose migraine.
The primary endpoint is a sensitivity of 79% and a specificity of 72% in distinguishing migraine with and without aura screened using the ICHD-3 benchmark criteria as compared to normal controls.
Time frame: 2 years
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