Chronic Migraine, Migraine, Tension-Type Headache
Conditions
Keywords
EEG, Machine Learning, Migraine, Headache, Diagnosis, Artificial Intelligence
Brief summary
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.
Interventions
DIAGNOSTIC_TESTMachine Learning Test for Migraine
Resting EEG, and Visual and Auditory Stimulation
Sponsors
Headache Sciences Incorporated
Study design
Observational model
OTHER
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 70 Years
Healthy volunteers
Yes
Inclusion criteria
* ● Age range: All participants and controls will be adults over 18, and no greater than 70 * Sex: Migraine patients and controls to reflect the demographics of the disease such that there is a balanced mix of males to females that reflects known female to male ratio for migraine is 3 to 1. * Capable of giving clear and reliable answers on the questionnaires * All potential participants should be able to read, write and speak in English: unless they can bring a translator. * All mentally competent: to give accurate answers on questionnaires, to decide for themselves and sign the informed consent form. * All medically stable patients. * Capable of safely using a staircase to our downstairs lab * OHIP must be up-to-date and participants must show a valid OHIP number * Able and willing to comply with all study requirements * Normal controls must be very healthy.
Exclusion criteria
* ● Under 18 years old * Over 70 years old (71 and above) * Incompetent in english language and no translator (This applies to reading, writing and speaking.) * Current moderate or serious mental illness (including depression, anxiety disorder, psychosis) * Mentally disabled/ Mentally incompetentHistory of photo-epilepsy or history of seizure following shortly after visual stimulus such as bright or repetitive flashes of light, repetitive flashes of light (such as at a disco, or looking at an overhead fan), or after looking at repetitive visual patterns lines such as looking at multiple stripes or squares, or from seeing movement such as when watching an action movie. * Medically unstable * Prisoner * Unable to go down the stairs (if recording is downstairs) * Pacemaker * Defibrillator * Cochlear implants * Significant skull deformity (e.g. a depression of greater than half an inch * Head injury with consciousness loss in the last year, even with apparent full recovery. * Severe facial trauma within the last three months, or longer if unrecovered. * Unnatural material inside the head or mouth based on history (including clips from surgery, shrapnel, bullet, medical pump, wires, medical device, metal dental implants) * Chronic communicable diseases carried (including Hepatitis B, Hepatitis C, HIV, Creutzfeldt-Jakob Disease) Note: Vagal stimulator and insulin pump are not an
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Feasibility of machine learning as applied to EEG to diagnose migraine. | 2 years | 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. |
Countries
Canada
Contacts
Primary ContactMark S. Doidge, MD, BA
Backup ContactGaurav Anand, MD, BSc
Outcome results
None listed