ORCID
- Al-Juboori, Shaymaa: 0000-0001-5175-736X
- Sun, Lingfen: 0000-0002-9921-2817
- Ifeachor, Emmanuel: 0000-0001-8362-6292
Abstract
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).
DOI
10.3390/brainsci11081026
Publication Date
2021-07-27
Publication Title
Brain Sciences
Volume
11
Issue
8
First Page
1026
Last Page
1026
Embargo Period
2021-10-16
Organisational Unit
School of Engineering, Computing and Mathematics
Recommended Citation
Al-Nuaimi, A. H., Blūma, M., Al-Juboori, S., Eke, C., Jammeh, E., Sun, L., & Ifeachor, E. (2021) 'Robust EEG Based Biomarkers to Detect Alzheimer’s Disease', Brain Sciences, 11(8), pp. 1026-1026. Available at: https://doi.org/10.3390/brainsci11081026
