Journal article
Phase lag index and spectral power as QEEG features for identification of patients with mild cognitive impairment in Parkinson's disease.
- Chaturvedi M Department of Neurology, University Hospital Basel, Basel, Switzerland; Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland.
- Bogaarts JG Department of Neurology, University Hospital Basel, Basel, Switzerland; Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland.
- Kozak Cozac VV Department of Neurology, University Hospital Basel, Basel, Switzerland; Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland.
- Hatz F Department of Neurology, University Hospital Basel, Basel, Switzerland.
- Gschwandtner U Department of Neurology, University Hospital Basel, Basel, Switzerland.
- Meyer A Department of Neurology, University Hospital Basel, Basel, Switzerland.
- Fuhr P Department of Neurology, University Hospital Basel, Basel, Switzerland.
- Roth V Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland. Electronic address: Volker.Roth@unibas.ch.
- 2019-08-25
Published in:
- Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology. - 2019
Connectivity
Machine learning
Mild cognitive impairment
Parkinson's disease
QEEG
Spectral power
Aged
Aged, 80 and over
Cognitive Dysfunction
Electroencephalography
Female
Humans
Male
Middle Aged
Parkinson Disease
English
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.
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.
- Language
-
- English
- Open access status
- hybrid
- Identifiers
-
- DOI 10.1016/j.clinph.2019.07.017
- PMID 31445388
- Persistent URL
- https://sonar.ch/global/documents/124825
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