EEG biomarkers for Alzheimer's disease: A novel automated pipeline for detecting and monitoring disease progression

BackgroundAlzheimer's disease (AD) is a neurodegenerative disorder that profoundly alters brain function and organization. Currently, there is a lack of validated functional biomarkers to aid in diagnosing and classifying AD. Therefore, there is a pressing need for early, accurate, non-invasive, and accessible methods to detect and characterize disease progression. Electroencephalography (EEG) has emerged as a minimally invasive technique to quantify functional changes in neural activity associated with AD. However, challenges such as poor signal-to-noise ratio-particularly for resting-state (rsEEG) recordings-and issues with standardization have hindered its broader application.ObjectiveTo conduct a pilot analysis of our custom automated preprocessing and feature extraction pipeline to identify indicators of AD and correlates of disease progression.MethodsWe analyzed data from 36 individuals with AD and 29 healthy participants recorded using a standard 19-channel EEG and features were processed using our custom end-t-end pipeline. Various features encompassing amplitude, power, connectivity, complexity, and microstates were extracted. Unsupervised machine learning (uniform manifold approximation and projection) and supervised learning (random forest classifiers with nested cross-validation) were used to characterize the dataset and identify differences between AD and healthy groups.ResultsOur pipeline successfully detected several new and previously established EEG-based measures indicative of AD status and progression, demonstrating strong external validity.ConclusionsOur findings suggest that this automated approach provides a promising initial framework for implementing EEG biomarkers in the AD patient population, paving the way for improved diagnostic and monitoring strategies.