Objective: To evaluate QEEG measures for classification of Parkinson’s disease(PD) patients and healthy individuals, and eventually develop a pre-clinical composite biomarker to identify patients with imminent PD.

Background: In recent years, certain QEEG parameters have been seen to be associated with dementia in Parkinson’s and Alzheimer’s Disease. A study conducted by Schmidt. et al (2013) evaluated quantitative EEG measures to determine a screening index to discriminate Alzheimer’s disease (AD) patients from healthy individuals. They found Alpha/Theta ratio to be a good measure for the same. QEEG measures have also been investigated for progression of PD cognitive states by Caviness et. al(2015).

Methods: High-resolution 256-channel EEG were recorded in 50 PD patients (age 68.8±7.0y; female/male 17/33) and 41 healthy controls (age 71.1±7.7y; female/male 20/22). Semi-automatic processing of the data was done to calculate the relative power in alpha, theta, delta, beta frequency bands across the different regions of the brain. Logistic regression using Lasso was applied to the data in R and the cross-validated receiver operating characteristic (ROC) curves were plotted.

Results: A group of seven measures was seen to have the most effect in differentiating healthy individuals from PD patients. They include: Frontal Left region in Delta band, Temporal Left in Theta band, Central left, Occipital regions in the Alpha1 band and the Parietal regions of the Beta band. The ROC curve showed an area under the curve of 0.76.

Conclusions: In this test sample, the regression methods applied to QEEG data were helpful in separating PD patients from healthy controls. However, a larger group of participants with distinct outcome variables would be needed for further studies.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/evaluating-quantitative-eeg-qeeg-measures-to-differentiate-between-parkinsons-disease-pd-patients-and-healthy-individuals/