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

Abstract

Alzheimer’s disease (AD) is a neurodegenerative brain disorder that affects cognitive functioning and memory. Current diagnostic tools, including neuroimaging techniques and cognitive questionnaires, present limitations such as invasiveness, high costs, and subjectivity. In recent years, interest has grown in using electroencephalography (EEG) for AD detection due to its non-invasiveness, low cost, and high temporal resolution. In this regard, this work introduces a novel metric for AD detection by using multiscale fuzzy entropy (MFE) to assess brain complexity, offering clinicians an objective, cost-effective diagnostic tool to aid early intervention and patient care. To this purpose, brain entropy patterns in different frequency bands for 35 healthy subjects (HS) and 35 AD patients were investigated. Then, based on the resulting MFE values, a specific detection algorithm, able to assess brain complexity abnormalities that are typical of AD, was developed and further validated on 24 EEG test recordings. This MFE-based method achieved an accuracy of 83% in differentiating between HS and AD, with a diagnostic odds ratio of 25, and a Matthews correlation coefficient of 0.67, indicating its viability for AD diagnosis. Furthermore, the algorithm showed potential for identifying anomalies in brain complexity when tested on a subject with mild cognitive impairment (MCI), warranting further investigation in future research.

EEG Data Analysis Techniques for Precision Removal and Enhanced Alzheimer’s Diagnosis: Focusing on Fuzzy and Intuitionistic Fuzzy Logic Techniques

“…The usefulness of EEG for the diagnosis and monitoring of AD is mainly linked to its ability to detect specific neurophysiological markers that indicate functional brain alterations, such as the slowing of global electrical activity of the brain as evidenced by changes in frequency bands named δ, θ, α, β, and γ [32]. The band δ (0.5 ÷ 4 Hz) signals slow brain activity linked to cortical damage, θ (4 ÷ 8 Hz) indicates transitions between sleep and wakefulness suggesting potential dysfunctions, α (8 ÷ 12 Hz) is associated with resting states and reflects the alteration of brain organization in AD, and β (12 ÷ 30 Hz) highlights levels of attention and mental activity, which is useful for observing cognitive changes in the patient [32,33]. Finally, the γ rhythm, above 30 Hz, is associated with complex cognitive processes such as object recognition and meaning attribution, and it is mainly detectable in the frontal regions [32][33][34][35][36][37].…”

“…The band δ (0.5 ÷ 4 Hz) signals slow brain activity linked to cortical damage, θ (4 ÷ 8 Hz) indicates transitions between sleep and wakefulness suggesting potential dysfunctions, α (8 ÷ 12 Hz) is associated with resting states and reflects the alteration of brain organization in AD, and β (12 ÷ 30 Hz) highlights levels of attention and mental activity, which is useful for observing cognitive changes in the patient [32,33]. Finally, the γ rhythm, above 30 Hz, is associated with complex cognitive processes such as object recognition and meaning attribution, and it is mainly detectable in the frontal regions [32][33][34][35][36][37]. Detailed EEG analysis, which includes the observation of specific changes in frequency bands, helps define a neurophysiological profile of AD [38][39][40].…”