Preoperative Electroencephalography-Based Machine Learning Predicts Cognitive Deterioration After Subthalamic Deep Brain Stimulation.

ABSTRACT:

Background

Subthalamic deep brain stimulation (STN DBS) may relieve refractory motor complications in Parkinson's disease (PD) patients. Despite careful screening, it remains difficult to determine severity of alpha-synucleinopathy involvement which influences the risk of postoperative complications including cognitive deterioration. Quantitative electroencephalography (qEEG) reflects cognitive dysfunction in PD and may provide biomarkers of postoperative cognitive decline.

Objective

To develop an automated machine learning model based on preoperative EEG data to predict cognitive deterioration 1 year after STN DBS.

Methods

Sixty DBS candidates were included; 42 patients had available preoperative EEGs to compute a fully automated machine learning model. Movement Diso

SUBMITTER: Geraedts VJ 

PROVIDER: S-EPMC8596544 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature