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低幅度颅面肌电图功率谱密度与基于磁共振成像的三维肌肉重建

Low-Amplitude Craniofacial EMG Power Spectral Density and 3D Muscle Reconstruction from MRI.

作者信息

Wiedemann Lukas, Chaberova Jana, Edmunds Kyle, Einarsdóttir Guðrún, Ramon Ceon, Gargiulo Paolo

机构信息

Institute for Biomedical and Neural Engineering, Háskólinn í Reykjavík, Menntavegur 1, 101 Reykjavík, Iceland; University of Applied Sciences, Höchstädtplatz 6, 1200 Wien, Austria.

Institute for Biomedical and Neural Engineering, Háskólinn í Reykjavík, Menntavegur 1, 101 Reykjavík, Iceland; Faculty of Electrical Engineering, Czech Technical University in Prague, Zikova 1903/4, 166 36 Praha 6, Czech Republic.

出版信息

Eur J Transl Myol. 2015 Mar 11;25(2):4886. doi: 10.4081/ejtm.2015.4886.

Abstract

Improving EEG signal interpretation, specificity, and sensitivity is a primary focus of many current investigations, and the successful application of EEG signal processing methods requires a detailed knowledge of both the topography and frequency spectra of low-amplitude, high-frequency craniofacial EMG. This information remains limited in clinical research, and as such, there is no known reliable technique for the removal of these artifacts from EEG data. The results presented herein outline a preliminary investigation of craniofacial EMG high-frequency spectra and 3D MRI segmentation that offers insight into the development of an anatomically-realistic model for characterizing these effects. The data presented highlights the potential for confounding signal contribution from around 60 to 200 Hz, when observed in frequency space, from both low and high-amplitude EMG signals. This range directly overlaps that of both low γ (30-50 Hz) and high γ (50-80 Hz) waves, as defined traditionally in standatrd EEG measurements, and mainly with waves presented in dense-array EEG recordings. Likewise, average EMG amplitude comparisons from each condition highlights the similarities in signal contribution of low-activity muscular movements and resting, control conditions. In addition to the FFT analysis performed, 3D segmentation and reconstruction of the craniofacial muscles whose EMG signals were measured was successful. This recapitulation of the relevant EMG morphology is a crucial first step in developing an anatomical model for the isolation and removal of confounding low-amplitude craniofacial EMG signals from EEG data. Such a model may be eventually applied in a clinical setting to ultimately help to extend the use of EEG in various clinical roles.

摘要

提高脑电图(EEG)信号的解读能力、特异性和敏感性是当前许多研究的主要重点,而EEG信号处理方法的成功应用需要详细了解低振幅、高频颅面肌电图(EMG)的地形图和频谱。在临床研究中,这些信息仍然有限,因此,目前还没有已知的可靠技术从EEG数据中去除这些伪迹。本文给出的结果概述了对颅面EMG高频频谱和三维磁共振成像(MRI)分割的初步研究,该研究为建立一个用于表征这些效应的解剖学真实模型提供了思路。所呈现的数据突出了在频率空间中观察到的,来自低振幅和高振幅EMG信号的60至200Hz左右的混杂信号贡献的可能性。这个范围与传统标准EEG测量中定义的低γ波(30 - 50Hz)和高γ波(50 - 80Hz)的范围直接重叠,并且主要与密集阵列EEG记录中呈现的波重叠。同样,每种情况下的平均EMG振幅比较突出了低活动肌肉运动和静息对照条件下信号贡献的相似性。除了进行快速傅里叶变换(FFT)分析外,对其EMG信号进行测量的颅面肌肉的三维分割和重建也取得了成功。这种对相关EMG形态的重现是开发一个解剖模型的关键的第一步,该模型用于从EEG数据中分离和去除混杂的低振幅颅面EMG信号。这样的模型最终可能会应用于临床环境,以最终帮助扩大EEG在各种临床角色中的应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a35/4749011/632f0a088375/ejtm-2015-2-4886-g001.jpg

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