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This pilot study applied Permutation Entropy (PE), a non-linear symbolic measure, and a novel modification (modPE), to investigate the regularity of electroencephalogram (EEG) signals from 11 Alzheimer’s disease (AD) patients and 11 age-matched controls given input parameters n (embedding vector), τ (coarse graining) and slide (difference between the start of two concurrent embedding vectors). PE discriminated better than modPE with controls showing reduced regularity over AD patients. Increasing τ identified the greatest differences between EEG signals. Longer embedding vectors were also more able to identify differences. The greatest difference between groups was at Fp1 with n,τ,slide = 3,10,1 (p=0.0112 Kruskal Wallis with Bonferroni). Subject and epoch based leave-one-out cross validation was carried out with thresholding from Receiver Operating Characteristic Curves. The greatest ability to correctly identify AD patients and controls were 81.82% (Fp2 n,τ,slide = 7,4,4, PE and modPE, F7 n,τ,slide = 3,10,1, PE and modPE) and 90.91% (Fp1 n,τ,slide = 3,10,1, PE and modPE), respectively. The maximum accuracy (both groups correctly identified) was 81.82% seen at many electrode and input combinations. All are with subject based analysis. This suggests that PE can identify changes in EEG signals in AD, given appropriate variables. However, modPE makes little improvement over PE.

Permutation Entropy of the Electroencephalogram Background Activity in Alzheimer’s Disease - Investigation into the Incidence of Repeated Values