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独立成分分析方法在大脑中表现出的全身性癫痫发作障碍活动中的应用与评估

Application and Evaluation of Independent Component Analysis Methods to Generalized Seizure Disorder Activities Exhibited in the Brain.

作者信息

George S Thomas, Balakrishnan R, Johnson J Stanly, Jayakumar J

机构信息

1 School of Electrical Sciences, Karunya University, Coimbatore, Tamil Nadu India.

2 Department of Neurology, PSG Institute of Medical Sciences and Research, Coimbatore, Tamil Nadu, India.

出版信息

Clin EEG Neurosci. 2017 Jul;48(4):295-300. doi: 10.1177/1550059416677915. Epub 2016 Nov 11.

Abstract

EEG records the spontaneous electrical activity of the brain using multiple electrodes placed on the scalp, and it provides a wealth of information related to the functions of brain. Nevertheless, the signals from the electrodes cannot be directly applied to a diagnostic tool like brain mapping as they undergo a "mixing" process because of the volume conduction effect in the scalp. A pervasive problem in neuroscience is determining which regions of the brain are active, given voltage measurements at the scalp. Because of which, there has been a surge of interest among the biosignal processing community to investigate the process of mixing and unmixing to identify the underlying active sources. According to the assumptions of independent component analysis (ICA) algorithms, the resultant mixture obtained from the scalp can be closely approximated by a linear combination of the "actual" EEG signals emanating from the underlying sources of electrical activity in the brain. As a consequence, using these well-known ICA techniques in preprocessing of the EEG signals prior to clinical applications could result in development of diagnostic tool like quantitative EEG which in turn can assist the neurologists to gain noninvasive access to patient-specific cortical activity, which helps in treating neuropathologies like seizure disorders. The popular and proven ICA schemes mentioned in various literature and applications were selected (which includes Infomax, JADE, and SOBI) and applied on generalized seizure disorder samples using EEGLAB toolbox in MATLAB environment to see their usefulness in source separations; and they were validated by the expert neurologist for clinical relevance in terms of pathologies on brain functionalities. The performance of Infomax method was found to be superior when compared with other ICA schemes applied on EEG and it has been established based on the validations carried by expert neurologist for generalized seizure and its clinical correlation. The results are encouraging for furthering the studies in the direction of developing useful brain mapping tools using ICA methods.

摘要

脑电图(EEG)通过放置在头皮上的多个电极记录大脑的自发电活动,并提供与大脑功能相关的丰富信息。然而,由于头皮中的容积传导效应,电极信号会经历“混合”过程,因此不能直接应用于诸如脑图谱之类的诊断工具。神经科学中一个普遍存在的问题是,在已知头皮电压测量值的情况下,确定大脑的哪些区域是活跃的。因此,生物信号处理领域对研究混合和解混合过程以识别潜在的活跃源产生了浓厚兴趣。根据独立成分分析(ICA)算法的假设,从头皮获得的混合信号可以通过大脑中电活动潜在源发出的“实际”脑电信号的线性组合来近似。因此,在临床应用之前,使用这些著名的ICA技术对脑电信号进行预处理,可能会开发出如定量脑电图这样的诊断工具,进而帮助神经科医生无创获取患者特定的皮层活动,这有助于治疗癫痫等神经病理学疾病。从各种文献和应用中挑选出了常用且经过验证的ICA方案(包括Infomax、JADE和SOBI),并在MATLAB环境中使用EEGLAB工具箱将其应用于全身性癫痫障碍样本,以观察它们在源分离中的效用;专家神经科医生根据大脑功能病理学方面的临床相关性对其进行了验证。结果发现,与应用于脑电图的其他ICA方案相比,Infomax方法的性能更优,并且基于专家神经科医生对全身性癫痫及其临床相关性的验证得以确立。这些结果对于推动使用ICA方法开发有用的脑图谱工具的研究方向而言是令人鼓舞的。

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