PET-validated EEG-Machine learning algorithm predicts brain amyloid pathology in pre-dementia alzheimer's disease...International Society for Gerontechnology 13th World Conference, October 22-26, 2022, Daegu, South Korea
Purpose: Developing reliable biomarkers is important for screening Alzheimer's disease (AD) and monitoring its progression. Although EEG is non-invasive direct measurement of brain neural activity and has potentials for various neurologic disorders, vulnerability to noise, difficulty in clinical int...
| Publicado en: | Gerontechnology Vol. 21; pp. 2 - 3 |
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| Autores principales: | , , |
| Formato: | abstract proceedings research Journal Article |
| Publicado: |
International Society for Gerontechnology
Oct2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=161396161&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 161396161 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15691101 904R jtl: Gerontechnology issn: 15691101 maglogo: N pubinfo: dt: Oct2022 vid: 21 pid: 54298 pub: International Society for Gerontechnology artinfo: ui: 161396161 161396161 161396161 10.4017/gt.2022.21.s.789.2.sp3 161396161 ppf: 2 ppct: 1 formats: tig: atl: PET-validated EEG-Machine learning algorithm predicts brain amyloid pathology in pre-dementia alzheimer's disease...International Society for Gerontechnology 13th World Conference, October 22-26, 2022, Daegu, South Korea aug: au: Kim, N. H. Park, U. Kang, S. W. affil: IMediSync Inc., Yeoksam-ro 175, Gangnam-gu, Seoul, Republic of Korea sug: subj: Alzheimer's Disease Diagnosis Brain Pathology Amyloid Neuropathies Algorithms Electroencephalography Biological Markers Disease Progression Congresses and Conferences South Korea South Korea Human ab: Purpose: Developing reliable biomarkers is important for screening Alzheimer's disease (AD) and monitoring its progression. Although EEG is non-invasive direct measurement of brain neural activity and has potentials for various neurologic disorders, vulnerability to noise, difficulty in clinical interpretation and quantification of signal information have limited its clinical application. There have been many research about machine learning (ML) adoption with EEG, but the accuracy of detecting AD is not so high or not validated with Aβ PET scan. We developed EEG-ML algorithm to detect brain Aβ pathology among subjective cognitive decline (SCD) or mild cognitive impairment (MCI) population, and validated it with Aβ PET. Methods: 19-channel resting-state EEG and Aβ PET were collected from 311 subjects: 196 SCD(36 Aβ+, 160 Aβ-), 115 MCI(54 Aβ+, 61Aβ-). 235 EEG data were used for training ML, and 76 for validation. EEG features were standardized for age and sex. Multiple important features sets were selected by 6 statistics analysis. Then, we trained 8 multiple machine learning for each important features set. Meanwhile, we conducted paired t-test to find statistically different features between amyloid positive and negative group. Results & Discussion: The best model showed 90.9% sensitivity, 76.7% specificity and 82.9% accuracy in MCI+SCD (33 Aβ+, 43 Aβ-). Limited to SCD, 92.3% sensitivity, 75.0% specificity, 81.1% accuracy (13 Aβ+, 24 Aβ-). 90% sensitivity, 78.9% specificity and 84.6% accuracy for MCI (20 Aβ+, 19 Aβ-). Similar trends of EEG power have been observed from the group comparison between Aβ+ and Aβ-, and between MCI and SCD: enhancement of frontal/ frontotemporal theta; attenuation of mid-beta in centroparietal areas. The present findings suggest that accurate classification for beta-amyloid accumulation in the brain based on QEEG alone could be possible, which implies that QEEG is a promising biomarker for beta-amyloid. Since QEEG is more accessible, cost-effective, and safer than amyloid PET, QEEG-based biomarkers may play an important role in the diagnosis and treatment of AD. We expect specific patterns in QEEG could play an important role to predict future progression of cognitive impairment in the preclinical stage of AD. Further feature engineering and validation with larger dataset is recommended. pubtype: Academic Journal doctype: abstract proceedings research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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