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用于睡眠评估的耳部脑电图:与活动记录仪和多导睡眠图的比较。

Ear-EEG for sleep assessment: a comparison with actigraphy and PSG.

作者信息

Tabar Yousef Rezaei, Mikkelsen Kaare B, Rank Mike Lind, Hemmsen Martin Christian, Otto Marit, Kidmose Preben

机构信息

Department of Electrical and Computer Engineering, Aarhus University, Finlandsgade 22, Building 5125, 8200, Aarhus, Denmark.

UNEEG Medical A/S, Lynge, Denmark.

出版信息

Sleep Breath. 2021 Sep;25(3):1693-1705. doi: 10.1007/s11325-020-02248-1. Epub 2020 Nov 21.

DOI:10.1007/s11325-020-02248-1
PMID:33219908
Abstract

PURPOSE

To assess automatic sleep staging of three ear-EEG setups with different electrode configurations and compare performance with concurrent polysomnography and wrist-worn actigraphy recordings.

METHODS

Automatic sleep staging was performed for single-ear, single-ear with ipsilateral mastoid, and cross-ear electrode configurations, and for actigraphy data. The polysomnography data were manually scored and used as the gold standard. The automatic sleep staging was tested on 80 full-night recordings from 20 healthy subjects. The scoring performance and sleep metrics were determined for all ear-EEG setups and the actigraphy device.

RESULTS

The single-ear, the single-ear with ipsilateral mastoid setup, and the cross-ear setup performed five class sleep staging with kappa values 0.36, 0.63, and 0.72, respectively. For the single-ear with mastoid electrode and the cross-ear setup, the performance of the sleep metrics, in terms of mean absolute error, was better than the sleep metrics estimated from the actigraphy device in the current study, and also better than current state-of-the-art actigraphy studies.

CONCLUSION

A statistically significant improvement in both accuracy and kappa was observed from single-ear to single-ear with ipsilateral mastoid, and from single-ear with ipsilateral mastoid to cross-ear configurations for both two and five-sleep stage classification. In terms of sleep metrics, the results were more heterogeneous, but in general, actigraphy and single-ear with ipsilateral mastoid configuration were better than the single-ear configuration; and the cross-ear configuration was consistently better than both the actigraphy device and the single-ear configuration.

摘要

目的

评估三种具有不同电极配置的耳部脑电图(ear-EEG)设置的自动睡眠分期,并将其性能与同步多导睡眠图和腕部活动记录仪记录进行比较。

方法

对单耳、单耳加同侧乳突以及双耳交叉电极配置进行自动睡眠分期,并对活动记录仪数据进行分期。多导睡眠图数据进行人工评分并用作金标准。在20名健康受试者的80份全夜记录上测试自动睡眠分期。确定所有耳部脑电图设置和活动记录仪的评分性能及睡眠指标。

结果

单耳、单耳加同侧乳突设置以及双耳交叉设置进行五分类睡眠分期的kappa值分别为0.36、0.63和0.72。对于单耳加乳突电极和双耳交叉设置,在平均绝对误差方面,睡眠指标的性能优于本研究中活动记录仪估计的睡眠指标,也优于当前最先进的活动记录仪研究。

结论

从单耳到单耳加同侧乳突,以及从单耳加同侧乳突到双耳交叉配置,在二分类和五分类睡眠分期中,准确性和kappa值均有统计学显著提高。在睡眠指标方面,结果更具异质性,但总体而言,活动记录仪和单耳加同侧乳突配置优于单耳配置;双耳交叉配置始终优于活动记录仪和单耳配置。

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本文引用的文献

1
Accurate whole-night sleep monitoring with dry-contact ear-EEG.干接触式耳 EEG 进行准确的全夜睡眠监测。
Sci Rep. 2019 Nov 14;9(1):16824. doi: 10.1038/s41598-019-53115-3.
2
International classification of sleep disorders-third edition: highlights and modifications.国际睡眠障碍分类第三版:要点和修改。
Chest. 2014 Nov;146(5):1387-1394. doi: 10.1378/chest.14-0970.
Sci Rep. 2024 Sep 19;14(1):21894. doi: 10.1038/s41598-024-72612-8.
4
From Scalp to Ear-EEG: A Generalizable Transfer Learning Model for Automatic Sleep Scoring in Older People.从头皮到耳朵-EEG:一种适用于老年人自动睡眠评分的可推广迁移学习模型。
IEEE J Transl Eng Health Med. 2024 Apr 17;12:448-456. doi: 10.1109/JTEHM.2024.3388852. eCollection 2024.
5
Flower electrodes for comfortable dry electroencephalography.用于舒适干脑电图的花形电极。
Sci Rep. 2023 Oct 3;13(1):16589. doi: 10.1038/s41598-023-42732-8.
6
At-home sleep monitoring using generic ear-EEG.使用通用耳部脑电图进行居家睡眠监测。
Front Neurosci. 2023 Feb 1;17:987578. doi: 10.3389/fnins.2023.987578. eCollection 2023.
7
Repeated automatic sleep scoring based on ear-EEG is a valuable alternative to manually scored polysomnography.基于耳部脑电图的重复自动睡眠评分是人工评分多导睡眠图的一种有价值的替代方法。
PLOS Digit Health. 2022 Oct 27;1(10):e0000134. doi: 10.1371/journal.pdig.0000134. eCollection 2022 Oct.
8
Sleep Spindle Characteristics and Relationship with Memory Ability in Patients with Obstructive Sleep Apnea-Hypopnea Syndrome.阻塞性睡眠呼吸暂停低通气综合征患者的睡眠纺锤波特征及其与记忆能力的关系
J Clin Med. 2023 Jan 12;12(2):634. doi: 10.3390/jcm12020634.
9
Association of Food Intake with Sleep Durations in Adolescents from a Capital City in Northeastern Brazil.巴西东北部首府青少年的饮食与睡眠时间的关联。
Nutrients. 2022 Dec 5;14(23):5180. doi: 10.3390/nu14235180.
10
Transcranial Auricular Vagus Nerve Stimulation (taVNS) and Ear-EEG: Potential for Closed-Loop Portable Non-invasive Brain Stimulation.经颅耳迷走神经刺激(taVNS)与耳脑电图:闭环便携式无创脑刺激的潜力
Front Hum Neurosci. 2021 Jun 14;15:699473. doi: 10.3389/fnhum.2021.699473. eCollection 2021.