Phase lag index and spectral power as QEEG features for identification of patients with mild cognitive impairment in Parkinson's disease

Abstract

Objectives: To identify quantitative EEG frequency and connectivity features (Phase Lag Index) characteristic of mild cognitive impairment (MCI) in Parkinson's disease (PD) patients and to investigate if these features correlate with cognitive measures of the patients.

Methods: We recorded EEG data for a group of PD patients with MCI (n = 27) and PD patients without cognitive impairment (n = 43) using a high-resolution recording system. The EEG files were processed and 66 frequency along with 330 connectivity (phase lag index, PLI) measures were calculated. These measures were used to classify MCI vs. MCI-free patients. We also assessed correlations of these features with cognitive tests based on comprehensive scores (domains).

Results: PLI measures classified PD-MCI from non-MCI patients better than frequency measures. PLI in delta, theta band had highest importance for identifying patients with MCI. Amongst cognitive domains, we identified the most significant correlations between Memory and Theta PLI, Attention and Beta PLI.

Conclusion: PLI is an effective quantitative EEG measure to identify PD patients with MCI.

Significance: We identified quantitative EEG measures which are important for early identification of cognitive decline in PD.

Keywords: Connectivity; Machine learning; Mild cognitive impairment; Parkinson's disease; QEEG; Spectral power.