Prospective Study Evaluating the EEG Recordings and Analysis in Parkinson's Patients: Towards Adaptive Deep Brain Stimulation by Machine Learning
Abstract
The objective of this protocol is to obtain on Parkinson's disease more accessible therapeutic targets than deep brain stimulation (HFS-STN), the neurosurgical treatment for this pathology. This study will pave the way for new forms of adaptive processing for the HFS-STN. It could become functionally coupled to a minimalist EEG centred on the motor cortex and to software for decoding, live or slightly delayed, classes of movements performed. On the one hand, this device could be used as a sensor of the quality of the information transmitted by the cortical network, thus allowing the selection of the optimal parameters of the HFS-STN on the basis of the movement decoding score. On the other hand, this device could lead to adapting the HFS-STN treatment over time by regularly calculating the recognition scores of the different movements performed and comparing them to the initial scores.
Conditions: Parkinson Disease.
Primary outcome: measure the encoding capacity of the cortical networks of parkinsonian patients, with or without anti-parkinsonian drug treatment, and with or without High-frequency stimulation (HFS) of the subthalamic nucleus (STN)..
Enrollment: 30 participants.
How strong is the evidence?
Evidence Hub reads this as a clinical trial, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 30 participants or records.
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