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日常临床实践中双极导联静息态脑电图的规范结构:一项初步研究。

Normative Structure of Resting-State EEG in Bipolar Derivations for Daily Clinical Practice: A Pilot Study.

作者信息

Pastor Jesús, Vega-Zelaya Lorena

机构信息

Clinical Neurophysiology and Instituto de Investigación Biomédica, Hospital Universitario de La Princesa, C/Diego de León 62, 28006 Madrid, Spain.

出版信息

Brain Sci. 2023 Jan 18;13(2):167. doi: 10.3390/brainsci13020167.

Abstract

We used numerical methods to define the normative structure of resting-state EEG (rsEEG) in a pilot study of 37 healthy subjects (10-74 years old), using a double-banana bipolar montage. Artifact-free 120-200 s epoch lengths were visually identified and divided into 1 s windows with a 10% overlap. Differential channels were grouped by frontal, parieto-occipital, and temporal lobes. For every channel, the power spectrum was calculated and used to compute the area for delta (0-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) bands and was log-transformed. Furthermore, Shannon's spectral entropy (SSE) and coherence by bands were computed. Finally, we also calculated the main frequency and amplitude of the posterior dominant rhythm. According to the age-dependent distribution of the bands, we divided the patients in the following three groups: younger than 20; between 21 and 50; and older than 51 years old. The distribution of bands and coherence was different for the three groups depending on the brain lobes. We described the normative equations for the three age groups and for every brain lobe. We showed the feasibility of a normative structure of rsEEG picked up with a double-banana montage.

摘要

在一项针对37名健康受试者(年龄在10至74岁之间)的初步研究中,我们采用数值方法,使用双香蕉双极导联,定义静息态脑电图(rsEEG)的标准结构。通过视觉识别出无伪迹的120 - 200秒时长的脑电片段,并将其划分为1秒的窗口,重叠率为10%。差分通道按额叶、顶枕叶和颞叶进行分组。对于每个通道,计算功率谱,并用于计算δ(0 - 4Hz)、θ(4 - 8Hz)、α(8 - 13Hz)和β(13 - 30Hz)频段的面积,并进行对数变换。此外,还计算了香农频谱熵(SSE)和各频段的相干性。最后,我们还计算了后头部优势节律的主频和振幅。根据频段的年龄依赖性分布,我们将患者分为以下三组:20岁以下;21至50岁之间;以及51岁以上。三组的频段分布和相干性因脑叶而异。我们描述了三个年龄组和每个脑叶的标准方程。我们展示了使用双香蕉导联获取rsEEG标准结构的可行性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b04/9953767/04b3efcb9911/brainsci-13-00167-g001.jpg

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