Entropy-Based EEG Measures for Revealing Altered Neural Dynamics in Alzheimer's Disease: A Preliminary Study

Alzheimer's disease (AD) is a prevalent age-related neurodegenerative condition, whose timely diagnosis is nowadays still complicated. In this context, investigating brain complexity can provide insight into the underlying mechanisms of neural dynamics in AD patients. In this regard, this study explores the differences in neural complexity between AD patients and healthy controls by using entropy measures. Specifically, multi-scale sample entropy (MSE) and multiscale approximate entropy (MAE) was computed on multi-channel electroencephalografic (EEG) signals at different time scales, highlighting the advantages and disadvantages of these two techniques, and outlining future improvements. Results show the potential of entropy measures as a promising strategy for early diagnosis of AD and for a better understanding of EEG dynamics alteration in AD, allowing for the development of more targeted therapies.