通过数据融合与匹配，用小样本实现高精度阿尔茨海默病风险预测。
Comprehensive Methodology for Sample Augmentation in EEG Biomarker Studies for Alzheimers Risk Classification
- 整合多源脑电数据，用倾向评分匹配平衡人群差异。
- 在2:1到10:1匹配比下，分类准确率达0.92至0.96。
- 适合神经退行性疾病早期筛查研究者参考。
痴呆症是全球健康挑战，阿尔茨海默病（AD）占约70%病例。脑电图（EEG）在识别AD风险方面有潜力，但大规模样本获取困难。本研究融合信号处理、数据调和与统计方法，提升样本量并增强风险分类可靠性。利用四个数据库的脑电数据，通过调和处理消除站点效应，同时保留年龄、性别等协变量。采用倾向评分匹配（PSM）设定2:1、5:1、10:1三种比例，评估样本量对模型性能的影响。最终数据集经决策树与交叉验证进行机器学习分析。结果表明，通过PSM平衡样本显著提升分类准确率，范围在0.92至0.96之间。该方法即使在样本有限时也能实现精准风险识别，为其他神经退行性疾病研究提供可行路径。
原文摘要 · Abstract (English)
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.
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