文献检索文档翻译深度研究
Suppr Zotero 插件Zotero 插件
邀请有礼套餐&价格历史记录

新学期,新优惠

限时优惠:9月1日-9月22日

30天高级会员仅需29元

1天体验卡首发特惠仅需5.99元

了解详情
不再提醒
插件&应用
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
高级版
套餐订阅购买积分包
AI 工具
文献检索文档翻译深度研究
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2025

夜间癫痫的脑电谱相干分析。

EEG Spectral Coherence Analysis in Nocturnal Epilepsy.

出版信息

IEEE Trans Biomed Eng. 2018 Dec;65(12):2713-2719. doi: 10.1109/TBME.2018.2814479. Epub 2018 Mar 9.


DOI:10.1109/TBME.2018.2814479
PMID:29993423
Abstract

OBJECTIVE: Electroencephalography (EEG) is widely employed in the study of sleep disorders. This paper exploits the identification of cyclic alternating patterns (CAPs), a periodic ubiquitous phenomenon nested in the sleep stages, to analyze the EEG spectral coherence in subjects affected by nocturnal frontal lobe epilepsy (NFLE) and healthy controls. METHODS: For each EEG recording, we extracted several CAP A1 subtype 4 s time series. We analyze the coherence between each pair of electrodes for each individual to obtain its distribution for each frequency range of interest to investigate differences between cases and controls. In addition, the imaginary and real parts of the spectral coherence were calculated and plotted to assess their likelihood of segregation into different classes and anatomical regions. RESULTS: The results of this study suggest a relevant frontal-temporal neural circuitry difference between individuals affected by epilepsy and controls. CONCLUSION: This supports the observation that, though highly variable, a broad range of executive, cognitive and attentional deficit observed in subjects affected by NFLE might depend on frontal-temporal altered networking. SIGNIFICANCE: The investigation of EEG activity in the domain of the complex sleep architecture represents a challenging topic in neurophysiology and needs new methods to explore the manifold aspects of sleep. This work aims to provide a simple method to distinguish NFLE from healthy subjects from a functional connectivity point of view and to explore the possibility of using a smaller EEG channel set to support diagnosis.

摘要

目的:脑电图(EEG)广泛应用于睡眠障碍的研究。本文利用周期性普遍存在的睡眠阶段中的循环交替模式(CAP)的识别,来分析受夜间额叶癫痫(NFLE)和健康对照影响的受试者的 EEG 频谱相干性。

方法:对于每个 EEG 记录,我们提取了几个 CAP A1 亚型 4s 时间序列。我们分析了每个个体的每个电极对之间的相干性,以获得其感兴趣的每个频率范围内的分布,以研究病例和对照组之间的差异。此外,计算并绘制了频谱相干性的虚部和实部,以评估它们是否有可能分为不同的类别和解剖区域。

结果:这项研究的结果表明,癫痫患者和对照组之间存在相关的额颞神经回路差异。

结论:这支持了这样一种观察,即尽管高度可变,但 NFLE 患者观察到的广泛的执行、认知和注意力缺陷可能取决于额颞叶改变的网络。

意义:在复杂睡眠结构领域研究 EEG 活动是神经生理学中的一个具有挑战性的课题,需要新的方法来探索睡眠的多方面。这项工作旨在从功能连接的角度提供一种简单的方法来区分 NFLE 和健康受试者,并探索使用更小的 EEG 通道集来支持诊断的可能性。

相似文献

[1]
EEG Spectral Coherence Analysis in Nocturnal Epilepsy.

IEEE Trans Biomed Eng. 2018-3-9

[2]
Distinctive polysomnographic traits in nocturnal frontal lobe epilepsy.

Epilepsia. 2012-5-11

[3]
Prediction error connectivity: A new method for EEG state analysis.

Neuroimage. 2018-12-1

[4]
The FLEP scale in diagnosing nocturnal frontal lobe epilepsy, NREM and REM parasomnias: data from a tertiary sleep and epilepsy unit.

Epilepsia. 2008-9

[5]
EEG segmentation for improving automatic CAP detection.

Clin Neurophysiol. 2013-5-1

[6]
All-night EEG power spectral analysis of the cyclic alternating pattern components in young adult subjects.

Clin Neurophysiol. 2005-10

[7]
Effects of antiepileptic treatment on sleep and seizures in nocturnal frontal lobe epilepsy.

Sleep Med. 2013-6-7

[8]
CAPSCNet: A novel scattering network for automated identification of phasic cyclic alternating patterns of human sleep using multivariate EEG signals.

Comput Biol Med. 2023-9

[9]
The neurophysiological evaluation of nocturnal frontal lobe epilepsy.

Seizure. 1998-8

[10]
Identifying montages that best detect electrographic seizure activity during polysomnography.

Sleep. 2000-3-15

引用本文的文献

[1]
Dynamic topological data analysis: a novel fractal dimension-based testing framework with application to brain signals.

Front Neuroinform. 2024-7-12

[2]
Quantitative Analysis of Inter- and Intrahemispheric Coherence on Epileptic Electroencephalography Signal.

J Med Signals Sens. 2022-5-12

[3]
The Time-Robustness Analysis of Individual Identification Based on Resting-State EEG.

Front Hum Neurosci. 2021-9-13

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

推荐工具

医学文档翻译智能文献检索