%0 Journal Article
%T Brain Age Estimation from Overnight Sleep Electroencephalography with Multi-Flow Sequence Learning.
%A Zhang D
%A She Y
%A Sun J
%A Cui Y
%A Yang X
%A Zeng X
%A Qin W
%J Nat Sci Sleep
%V 16
%N 0
%D 2024
%M 38974693
%F 3.384
%R 10.2147/NSS.S463495
%X <B>UNASSIGNED: </B>This study aims to improve brain age estimation by developing a novel deep learning model utilizing overnight electroencephalography (EEG) data.<BR><B>UNASSIGNED: </B>We address limitations in current brain age prediction methods by proposing a model trained and evaluated on multiple cohort data, covering a broad age range. The model employs a one-dimensional Swin Transformer to efficiently extract complex patterns from sleep EEG signals and a convolutional neural network with attentional mechanisms to summarize sleep structural features. A multi-flow learning-based framework attentively merges these two features, employing sleep structural information to direct and augment the EEG features. A post-prediction model is designed to integrate the age-related features throughout the night. Furthermore, we propose a DecadeCE loss function to address the problem of an uneven age distribution.<BR><B>UNASSIGNED: </B>We utilized 18,767 polysomnograms (PSGs) from 13,616 subjects to develop and evaluate the proposed model. The model achieves a mean absolute error (MAE) of 4.19 and a correlation of 0.97 on the mixed-cohort test set, and an MAE of 6.18 years and a correlation of 0.78 on an independent test set. Our brain age estimation work reduced the error by more than 1 year compared to other studies that also used EEG, achieving the level of neuroimaging. The estimated brain age index demonstrated longitudinal sensitivity and exhibited a significant increase of 1.27 years in individuals with psychiatric or neurological disorders relative to healthy individuals.<BR><B>UNASSIGNED: </B>The multi-flow deep learning model proposed in this study, based on overnight EEG, represents a more accurate approach for estimating brain age. The utilization of overnight sleep EEG for the prediction of brain age is both cost-effective and adept at capturing dynamic changes. These findings demonstrate the potential of EEG in predicting brain age, presenting a noninvasive and accessible method for assessing brain aging.