Comprehensive Methodology for Sample Augmentation in EEG Biomarker Studies for Alzheimers Risk Classification
This work addresses the problem of reliable Alzheimer's disease risk classification using EEG data for medical researchers and clinicians, though it is incremental as it combines existing techniques in a novel way.
This study tackled the challenge of limited sample sizes in EEG biomarker studies for Alzheimer's disease risk classification by integrating signal processing, harmonization, and propensity score matching, resulting in classification accuracy improvements ranging from 0.92 to 0.96 across different matching ratios.
Background: Dementia, marked by cognitive decline, is a global health challenge. Alzheimer's disease (AD), the leading type, accounts for ~70% of cases. Electroencephalography (EEG) measures show promise in identifying AD risk, but obtaining large samples for reliable comparisons is challenging. Objective: This study integrates signal processing, harmonization, and statistical techniques to enhance sample size and improve AD risk classification reliability. Methods: We used advanced EEG preprocessing, feature extraction, harmonization, and propensity score matching (PSM) to balance healthy non-carriers (HC) and asymptomatic E280A mutation carriers (ACr). Data from four databases were harmonized to adjust site effects while preserving covariates like age and sex. PSM ratios (2:1, 5:1, 10:1) were applied to assess sample size impact on model performance. The final dataset underwent machine learning analysis with decision trees and cross-validation for robust results. Results: Balancing sample sizes via PSM significantly improved classification accuracy, ranging from 0.92 to 0.96 across ratios. This approach enabled precise risk identification even with limited samples. Conclusion: Integrating data processing, harmonization, and balancing techniques improves AD risk classification accuracy, offering potential for other neurodegenerative diseases.