Night-to-night variability of sleep electroencephalography-based brain age measurements.
Brain age
Brain health
EEG
Night-to-night variability
Sleep
Journal
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
ISSN: 1872-8952
Titre abrégé: Clin Neurophysiol
Pays: Netherlands
ID NLM: 100883319
Informations de publication
Date de publication:
01 2021
01 2021
Historique:
received:
07
01
2020
revised:
21
08
2020
accepted:
18
09
2020
pubmed:
29
11
2020
medline:
16
7
2021
entrez:
28
11
2020
Statut:
ppublish
Résumé
Brain Age Index (BAI), calculated from sleep electroencephalography (EEG), has been proposed as a biomarker of brain health. This study quantifies night-to-night variability of BAI and establishes probability thresholds for inferring underlying brain pathology based on a patient's BAI. 86 patients with multiple nights of consecutive EEG recordings were selected from Epilepsy Monitoring Unit patients whose EEGs reported as within normal limits. While EEGs with epileptiform activity were excluded, the majority of patients included in the study had a diagnosis of chronic epilepsy. BAI was calculated for each 12-hour segment of patient data using a previously established algorithm, and the night-to-night variability in BAI was measured. The within-patient night-to-night standard deviation in BAI was 7.5 years. Estimates of BAI derived by averaging over 2, 3, and 4 nights had standard deviations of 4.7, 3.7, and 3.0 years, respectively. Averaging BAI over n nights reduces night-to-night variability of BAI by a factor of n, rendering BAI a more suitable biomarker of brain health at the individual level. A brain age risk lookup table of results provides thresholds above which a patient has a high probability of excess BAI. With increasing ease of EEG acquisition, including wearable technology, BAI has the potential to track brain health and detect deviations from normal physiologic function. The measure of night-to-night variability and how this is reduced by averaging across multiple nights provides a basis for using BAI in patients' homes to identify patients who should undergo further investigation or monitoring.
Identifiants
pubmed: 33248430
pii: S1388-2457(20)30520-4
doi: 10.1016/j.clinph.2020.09.029
pmc: PMC7855943
mid: NIHMS1642206
pii:
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1-12Subventions
Organisme : NINDS NIH HHS
ID : R01 NS102190
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS107291
Pays : United States
Informations de copyright
Copyright © 2020 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest Dr. Thomas declares the following conflicts: 1) Patent and license for a technology using ECG to assess sleep quality and sleep apnea, licensed to MyCardio, LLC; 2) Patent and license for an auto-CPAP algorithm, licensed to DeVilbiss-Drive. 3) Unlicensed [patent to treat central sleep apnea with low concentration CO2 with positive airway pressure. 4) Consultant to Jazz Pharmaceuticals; 5) General sleep medicine consulting for GLG Councils and Guidepoint Global.
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