abstract
- Alzheimer's disease (AD) is one of the most common neurodegenerative disorders, affecting more than 50 million people worldwide. Current detection methods are often inefficient and inaccessible or invasive and inadequate for the early stages, as pathological biomarkers have not been developed. This study introduces a novel method to characterize hippocampal local field potentials (LFPs) to aid in the early detection of AD. We used fractal dimension (FD) analysis to process LFP recordings of the hippocampus region of animal models, recorded in both basal and kainic acid-induced active states. The LFP signals were classified using time-series clustering to identify the existence of more than one trend. The FD values of different states of an LFP in an AD model were analyzed. The results show that the transition phase triggers fluctuations in the FD value despite the basal- and active-state values remaining consistent. The findings of this analysis provide a promising basis for the development of a digital biomarker for conditions that disrupt normal brain behavior, such as AD. This approach could potentially improve our understanding of these diseases and contribute to more effective diagnostic tools. © 2024 IEEE.