Brain Age Estimation from Overnight Sleep Electroencephalography with Multi-Flow Sequence Learning

IF 3 2区医学 Q2 CLINICAL NEUROLOGY

Nature and Science of Sleep Pub Date : 2024-07-01 DOI:10.2147/nss.s463495

Di Zhang, Yichong She, Jinbo Sun, Yapeng Cui, Xuejuan Yang, Xiao Zeng, Wei Qin

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Abstract

Purpose: This study aims to improve brain age estimation by developing a novel deep learning model utilizing overnight electroencephalography (EEG) data.
Methods: 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.
Results: 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.
Conclusion: 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.

Keywords: brain age, sleep polysomnography, electroencephalography, deep learning, swin transformer

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利用多流序列学习从隔夜睡眠脑电图估算大脑年龄

目的：本研究旨在利用隔夜脑电图（EEG）数据开发一种新型深度学习模型，从而改进脑年龄估计方法：我们针对当前脑年龄预测方法的局限性，提出了一个在多个队列数据上进行训练和评估的模型，涵盖了广泛的年龄范围。该模型采用一维斯温变换器（Swin Transformer）从睡眠脑电信号中有效提取复杂模式，并利用具有注意机制的卷积神经网络总结睡眠结构特征。基于多流学习的框架将这两种特征进行了注意合并，利用睡眠结构信息来引导和增强脑电图特征。我们还设计了一个后预测模型，以整合整夜的年龄相关特征。此外，我们还提出了 DecadeCE 损失函数，以解决年龄分布不均的问题：我们利用来自 13,616 名受试者的 18,767 张多导睡眠图（PSG）来开发和评估所提出的模型。该模型在混合队列测试集上的平均绝对误差（MAE）为 4.19，相关性为 0.97；在独立测试集上的平均绝对误差为 6.18 岁，相关性为 0.78。与其他同样使用脑电图的研究相比，我们的脑年龄估计工作将误差减少了 1 岁以上，达到了神经影像学的水平。估算出的脑年龄指数表现出纵向敏感性，在患有精神或神经疾病的个体中，脑年龄指数比健康个体显著增加了1.27岁：结论：本研究中提出的基于夜间脑电图的多流深度学习模型是一种更准确的脑年龄估算方法。利用夜间睡眠脑电图预测脑年龄既经济又能捕捉动态变化。这些发现证明了脑电图在预测脑年龄方面的潜力，为评估脑衰老提供了一种无创、便捷的方法。关键词：脑年龄；睡眠多导睡眠图；脑电图；深度学习；斯温变换器

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来源期刊

Nature and Science of Sleep Neuroscience-Behavioral Neuroscience

CiteScore

5.70

自引率

5.90%

发文量

245

审稿时长

16 weeks

期刊介绍： Nature and Science of Sleep is an international, peer-reviewed, open access journal covering all aspects of sleep science and sleep medicine, including the neurophysiology and functions of sleep, the genetics of sleep, sleep and society, biological rhythms, dreaming, sleep disorders and therapy, and strategies to optimize healthy sleep. Specific topics covered in the journal include: The functions of sleep in humans and other animals Physiological and neurophysiological changes with sleep The genetics of sleep and sleep differences The neurotransmitters, receptors and pathways involved in controlling both sleep and wakefulness Behavioral and pharmacological interventions aimed at improving sleep, and improving wakefulness Sleep changes with development and with age Sleep and reproduction (e.g., changes across the menstrual cycle, with pregnancy and menopause) The science and nature of dreams Sleep disorders Impact of sleep and sleep disorders on health, daytime function and quality of life Sleep problems secondary to clinical disorders Interaction of society with sleep (e.g., consequences of shift work, occupational health, public health) The microbiome and sleep Chronotherapy Impact of circadian rhythms on sleep, physiology, cognition and health Mechanisms controlling circadian rhythms, centrally and peripherally Impact of circadian rhythm disruptions (including night shift work, jet lag and social jet lag) on sleep, physiology, cognition and health Behavioral and pharmacological interventions aimed at reducing adverse effects of circadian-related sleep disruption Assessment of technologies and biomarkers for measuring sleep and/or circadian rhythms Epigenetic markers of sleep or circadian disruption.