Development of a Medical Device Utilizing an EEG-Based Algorithm for the Objective Quantification of Pain
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Obtained 19 lead EEG data from 50 chronic pain patients along with their clinical history and self-reported pain scored. Machine learning was used to analyze the EEG data and derive a chronic pain biomarker.
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Layout table for eligibility information
Ages Eligible for Study:
18 Years to 85 Years (Adult, Older Adult)
Sexes Eligible for Study:
Accepts Healthy Volunteers:
Fifty (50) male and female pain patients with symptoms in excess of 3 months duration (per the IASP definition of Chronic Pain) between the ages of 18-85 years were enrolled. Subjects who met the inclusion/exclusion criteria were asked to volunteer for the study.
Male and female chronic pain patients
Patients between the ages of 18-85 years
Patients exhibiting the presence of symptoms in excess of 3 months duration
Patients suffering from neuropathic (e.g., lower back pain), osteoarthritis, or muscular skeletal pain
Patients with evidence of pathology related to the painful condition on which diagnosis was made (e.g., results of imaging or diagnostic pain code)
Patients with NRS pain scores across the full range (1-10) at the time of testing
Patients with medically diagnosed psychotic illness
Patients with medically diagnosed drug or alcohol dependence in the past 12 months
Patients with a medical history of head injury with loss of consciousness and amnesia (within the last 2 years)
Patients with skull abnormalities that preclude the proper placement of the electrodes for the EEG data acquisition
Patients who have a spinal cord stimulator, or other implantable devices
Patients for whom the source of pain at the time of the evaluation is associated with: neurological disorders (multiple sclerosis, Parkinson, dementia), diabetes, migraines, or those with reflex / sympathetic dystrophy disorder/complex regional pain syndrome, fibromyalgia, or visceral pain