Resting-State EEG Biomarkers in Obstructive Sleep Apnea: Case Detection by qEEG and Cognitive Profiling by Beta Burst Features
[EN] Obstructive sleep apnea (OSA) is associated with cognitive decline, yet scalable electrophysiological biomarkers for case detection and cognitive profiling remain limited. We analyzed task-free resting-state EEG (rsEEG) from 34 OSA patients and 13 matched controls to compare sustained spectral...
| Autores: | , , , , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:dnet:riunet______::12bd7115d4ca8c7f7cc08e92c13dc93c |
| Acceso en línea: | https://riunet.upv.es/handle/10251/234792 |
| Access Level: | acceso embargado |
| Palabra clave: | Obstructive sleep apnea Resting state electroencephalography Quantitative electroencephalography Beta burst Biomarkers Cognitive Impairment |
| Sumario: | [EN] Obstructive sleep apnea (OSA) is associated with cognitive decline, yet scalable electrophysiological biomarkers for case detection and cognitive profiling remain limited. We analyzed task-free resting-state EEG (rsEEG) from 34 OSA patients and 13 matched controls to compare sustained spectral measures of conventional quantitative EEG (qEEG) with transient beta burst features in millisecond-scale event detection. qEEG showed cortical slowing in OSA, characterized by increased theta activity, decreased beta activity, and a steeper spectral exponent slope, providing the most apparent separation between patients and controls. In contrast, transient beta burst features are indexed to cognition. Univariate correlation analyses identified beta burst rate as the single burst metric most highly associated with Montreal Cognitive Assessment (MoCA) scores, which is regionally strongest in bilateral temporal and fronto-central sites. Using a multivariate Lasso-regularized framework with repeated 5-fold cross-validation, beta burst feature sets yielded the strongest association with cognition (median rho = 0.79), outperforming qEEG features (median rho = 0.70). These results support a framework for OSA electrophysiology, where qEEG serves as a robust marker for case detection and beta burst dynamics, notably burst rate, as a sensitive marker for cognitive impairment. Both features are extractable from short, task-free rsEEG. They are readily integrable into clinical monitoring pipelines and closed-loop control systems for patient stratification, longitudinal tracking, and intervention targeting of OSA-related cognitive dysfunction. |
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