Identifying and differentiating dementias with EEG fractal dimension distributions

Abstract

AbstractBackgroundAlzheimer’s disease dementia (AD‐dementia) and non‐AD dementia have both been associated with decreases in fractal dimension and spectral slowing in electroencephalography (EEG). Here, we describe a set of EEG features ‐ Fractal Dimension Distributions (FDD) ‐ which quantifies the stability of fractal dimensions over time within specific frequency bands. We compare the performance of models trained using this FDD feature to models using age, spectral power, or overall fractal dimension.MethodWe analyzed resting‐state data from older adults (age M = 71, range = 55‐86) with subjective cognitive impairment (SCI; N = 97, 59 female) or dementia (N = 51, 32 female), including 38 with AD (N = 33). We calculated the Fractal Dimension Distributions (FDD) by first computing the Higuchi fractal dimension within moving windows, then summarizing the distribution of these fractal values using the mean and standard deviation within standard frequency bands. Finally, we trained regularized logistic regression models to classify participants as having SCI or dementia, and as having SCI or AD‐dementia. We compared the performance of models using FDD or standard measures of fractal dimension. Models were evaluated using balanced accuracy, weighted sensitivity, and weighted specificity calculated using 5‐fold cross‐validation.ResultFor SCI vs Dementia, the model using FDD (accuracy = 74%, sensitivity = 67%, specificity = 81%, p < .001) had higher accuracy, sensitivity, and specificity than models trained on age, standard measures of fractal dimension, or spectral power. Similarly, the model trained to predict SCI vs AD‐Dementia was most accurate when using FDD (accuracy = 73%, sensitivity = 59%, specificity = 97%, p < .005). The features useful for discriminating SCI from Dementia partially overlapped with the features useful for discriminating SCI from AD‐Dementia.ConclusionOur novel feature, FDD, outperforms standard measures of fractal dimensions or spectral band power at discriminating between SCI and dementia. Moreover, FDD may help to distinguish between AD and other varieties of dementia.