Background:
Early differentiation between Alzheimer’s disease (AD) and Dementia with
Lewy Bodies (DLB) is important for accurate prognosis, as DLB patients typically show
faster disease progression. Cortical neural networks, necessary for human cognitive function,
may be disrupted differently in DLB and AD patients, allowing diagnostic differentiation
between AD and DLB.
Objective:
This proof-of-concept study assessed whether the application of machine learning
techniques to data derived from resting-state electroencephalographic (rsEEG) rhythms
(discriminant sensor power, 19 electrodes) and source connectivity (between five cortical
regions of interest) allowed differentiation between DLB and AD.
Methods:
Clinical, demographic, and rsEEG datasets from DLB patients (N=30), AD patients
(N=30), and control seniors (NOld, N=30), matched for age, sex, and education, were taken
from our international database. Individual (delta, theta, alpha) and fixed (beta) rsEEG
frequency bands were included. The rsEEG features for the classification task were computed
at both sensor and source levels. The source level was based on eLORETA freeware
toolboxes for estimating cortical source activity and linear lagged connectivity. Fluctuations
of rsEEG recordings (band-pass waveform envelopes of each EEG rhythm) were also
computed at both sensor and source levels. After blind feature reduction, rsEEG features
served as input to support vector machine (SVM) classifiers. Discrimination of individuals
from the three groups was measured with standard performance metrics (accuracy,
sensitivity, and specificity).
Results:
The trained SVM two-class classifiers showed classification accuracies of 97.6% for
NOld vs. AD, 99.7% for NOld vs. DLB, and 97.8% for AD vs. DLB. Three-class classifiers
(AD vs. DLB vs. NOld) showed classification accuracy of 94.79%.
Conclusion:
These promising preliminary results should encourage future prospective and
longitudinal cross-validation studies using higher resolution EEG techniques and harmonized
clinical procedures to enable the clinical application of these machine learning techniques.
Random forest to differentiate dementia with Lewy bodies from Alzheimer's disease
