Electroencephalographia as Predictor of Effectiveness HD-tDCS in Neuropathic Pain: Machine Learning Approach
Last updated on July 2021Recruitment
- Recruitment Status
- Not yet recruiting
- Estimated Enrollment
- Same as current
Summary
- Conditions
- Chronic Pain
- Traumatic Brachial Plexus Lesion
- Type
- Observational
- Design
- Observational Model: CohortTime Perspective: Retrospective
Participation Requirements
- Age
- Between 18 years and 125 years
- Gender
- Both males and females
Description
Using connectivity-based prediction and machine learning, the objective is to assess whether characteristics related to baseline EEG predict the response of patients with neuropathic pain after BPI to the effectiveness of HD-tDCS treatment. An observational, retrospective cohort study will be carrie...
Using connectivity-based prediction and machine learning, the objective is to assess whether characteristics related to baseline EEG predict the response of patients with neuropathic pain after BPI to the effectiveness of HD-tDCS treatment. An observational, retrospective cohort study will be carried out, of predictive response with a quantitative approach, of an applied nature, of an exploratory and open-label type, related to the efficacy of HD-tDCS4x1 in patients with neuropathic pain due to BPI, from an analysis of data obtained from a pilot, placebo-controlled, triple-blind, randomized, crossover type clinical trial, in accordance with the CONSORT guidelines, which will investigate the effectiveness of treatment with HD-tDCS.
Tracking Information
- NCT #
- NCT04852536
- Collaborators
- Not Provided
- Investigators
- Principal Investigator: Suellen Andrade Federal University of Paraiba