Abstract
Alzheimer’s Disease (AD) is a progressive neurodegenerative condition causing memory, attention, and language decline. Current AD diagnostic methods lack objectivity and non-invasiveness. While electroencephalography (EEG) holds promise for AD research, conventional EEG analysis methods have proven unsatisfactory. Non-linear dynamical approaches are considered more effective in assessing the brain’s complex nature. Starting from these considerations, this study presents an entropy-based algorithm utilizing Multiscale Fuzzy Entropy (MFE) as a promising, effective AD diagnostic method. Computed across 20 different time scales for a public dataset, MFE showed a significant discriminative power. Notably, a trend inversion was observed in the results: AD subjects displayed higher complexity values for slow frequency bands compared to healthy controls, while the opposite was found in fast frequency bands. These findings underscore the potential of MFE in effectively distinguishing AD patients from healthy individuals, marking a significant advance towards more objective and reliable AD diagnosis strategies.
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