A systematic review and methodological analysis of EEG-based biomarkers of Alzheimer's disease

A Novel Metric for Alzheimer’s Disease Detection Based on Brain Complexity Analysis via Multiscale Fuzzy Entropy

“…Nowadays, some drugs can slow the progression of AD, but a definitive cure does not yet exist [3]. Therefore, methods capable of diagnosing, detecting, and monitoring AD at each stage are needed [4]. For this purpose, different tools have been employed, such as neuroimaging techniques (i.e., positron emission tomography-PET, magnetic resonance imaging-MRI), blood biomarkers, and cognitive questionnaires (i.e., the mini-mental state examination-MMSE) [2].…”

“…Indeed, several studies have tried to develop EEG-based biomarkers for AD [4] showing alterations in EEG dynamics. Specifically, a reduction in high-frequency activities (α, 8-13 Hz, and β, 13-30 Hz) and an increase in low-frequency activities (δ, 0.5-4 Hz, and θ, 4-8 Hz) have been observed in AD patients compared to healthy subjects [4,10]. Consequently, the ratio between δ and α power of EEG signals and the ratio between θ and α power have been applied for AD diagnosis [4].…”

“…Instead, brain processes are neither purely regular nor totally random [13]. Therefore, complexity measures for EEG signals may offer a novel perspective on understanding physiological processes in AD [4,14].…”

“…Although several studies have used different measures of EEG complexity over the years to assess the status of AD patients [4], a lack of standardized approaches and normative values persist in the assessment of the complexity of EEG signals.…”

“…Several studies in the literature provide compelling evidence of substantial alterations in entropy values within individuals with AD compared to control counterparts [4,19]. This suggests that the complexity of EEG signals provides detailed insights into neural mechanisms that other measurement approaches may not capture, resulting in a high potential for diagnostics and early detection of AD.…”