Robust EEG Based Biomarkers to Detect Alzheimer's Disease.
Robust EEG Based Biomarkers to Detect Alzheimer's Disease.
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DOI：
10.3390/brainsci11081026
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发表时间：
2021-07-31
期刊：
影响因子：
3.3
通讯作者：
Ifeachor E
中科院分区：
文献类型：
--
作者：
Al-Nuaimi AH;Blūma M;Al-Juboori SS;Eke CS;Jammeh E;Sun L;Ifeachor E
检测阿尔茨海默病（AD）的生物标志物将使患者能够获得适当的服务，并可能促进新疗法的开发。鉴于大量的人受AD影响，需要一种低成本、易于使用的方法来检测AD患者。潜在地，脑电图（EEG）可以在这方面发挥有价值的作用，但目前没有一个单一的EEG生物标志物是足够强大的用于实践。本研究旨在通过利用关键生物标志物的组合优势，为开发稳健的EEG生物标志物以临床可接受的性能检测AD提供方法学框架。大量的现有的和新的EEG生物标志物与减缓的EEG，降低EEG的复杂性和减少EEG连接进行了调查。支持向量机和线性判别分析方法被用来寻找最佳组合的EEG生物标志物检测AD具有显着的性能。共研究了325，567种EEG生物标志物，鉴定了一组6种生物标志物，并用于创建具有高性能的诊断模型（灵敏度≥85%，特异性100%）。
Biomarkers to detect Alzheimer’s disease (AD) would enable patients to gain access to appropriate services and may facilitate the development of new therapies. Given the large numbers of people affected by AD, there is a need for a low-cost, easy to use method to detect AD patients. Potentially, the electroencephalogram (EEG) can play a valuable role in this, but at present no single EEG biomarker is robust enough for use in practice. This study aims to provide a methodological framework for the development of robust EEG biomarkers to detect AD with a clinically acceptable performance by exploiting the combined strengths of key biomarkers. A large number of existing and novel EEG biomarkers associated with slowing of EEG, reduction in EEG complexity and decrease in EEG connectivity were investigated. Support vector machine and linear discriminate analysis methods were used to find the best combination of the EEG biomarkers to detect AD with significant performance. A total of 325,567 EEG biomarkers were investigated, and a panel of six biomarkers was identified and used to create a diagnostic model with high performance (≥85% for sensitivity and 100% for specificity).
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