期刊
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
卷 15, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fncom.2021.755499
关键词
EEG; Alzheimer's disease (AD); beta-amyloid; machine learning; diagnosis; genetic algorithm; pre-dementia Alzheimer's disease
资金
- Research and Business Development Program through the Korea Institute for Advancement of Technology (KIAT) - Ministry of Trade, Industry and Energy (MOTIE) [P0014055]
- Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI)
- Korea Dementia Research Center (KDRC) - Ministry of Health Welfare
- Ministry of Science and ICT, South Korea [HU20C0511000020]
- Korean Ministry of Health Welfare [HI18C0530]
- Brain Convergence Research Program of the National Research Foundation - Ministry of Science and ICT, South Korea [NRF-2020M3E5D2A01084721]
- Korea Evaluation Institute of Industrial Technology (KEIT) [P0014055] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- National Research Foundation of Korea [2020M3E5D2A01084721] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
The study utilized a qEEG-ML algorithm to predict A beta pathology, achieving high sensitivity, specificity, and accuracy in classifying amyloid positive/negative cases in patients with subjective cognitive decline and mild cognitive impairment. The analysis of EEG data from different frequency bands contributed to the success of this classification method.
The use of positron emission tomography (PET) as the initial or sole biomarker of beta-amyloid (A beta) brain pathology may inhibit Alzheimer's disease (AD) drug development and clinical use due to cost, access, and tolerability. We developed a qEEG-ML algorithm to predict A beta pathology among subjective cognitive decline (SCD) and mild cognitive impairment (MCI) patients, and validated it using A beta PET. We compared QEEG data between patients with MCI and those with SCD with and without PET-confirmed beta-amyloid plaque. We compared resting-state eyes-closed electroencephalograms (EEG) patterns between the amyloid positive and negative groups using relative power measures from 19 channels (Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, O2), divided into eight frequency bands, delta (1-4 Hz), theta (4-8 Hz), alpha 1 (8-10 Hz), alpha 2 (10-12 Hz), beta 1 (12-15 Hz), beta 2 (15-20 Hz), beta 3 (20-30 Hz), and gamma (30-45 Hz) calculated by FFT and denoised by iSyncBrain(R). The resulting 152 features were analyzed using a genetic algorithm strategy to identify optimal feature combinations and maximize classification accuracy. Guided by gene modeling methods, we treated each channel and frequency band of EEG power as a gene and modeled it with every possible combination within a given dimension. We then collected the models that showed the best performance and identified the genes that appeared most frequently in the superior models. By repeating this process, we converged on a model that approximates the optimum. We found that the average performance increased as this iterative development of the genetic algorithm progressed. We ultimately achieved 85.7% sensitivity, 89.3% specificity, and 88.6% accuracy in SCD amyloid positive/negative classification, and 83.3% sensitivity, 85.7% specificity, and 84.6% accuracy in MCI amyloid positive/negative classification.
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