Normalized Power Variance: A new Field Orthogonal to Power in EEG Analysis.

Clinical EEG and neuroscience Pub Date : 2023-11-01 Epub Date: 2022-03-29 DOI:10.1177/15500594221088736
Yasunori Aoki, Hiroaki Kazui, Roberto D Pascual-Marqui, Ricardo Bruña, Kenji Yoshiyama, Tamiki Wada, Hideki Kanemoto, Yukiko Suzuki, Takashi Suehiro, Yuto Satake, Maki Yamakawa, Masahiro Hata, Leonides Canuet, Ryouhei Ishii, Masao Iwase, Manabu Ikeda
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引用次数: 3

Abstract

To date, electroencephalogram (EEG) has been used in the diagnosis of epilepsy, dementia, and disturbance of consciousness via the inspection of EEG waves and identification of abnormal electrical discharges and slowing of basic waves. In addition, EEG power analysis combined with a source estimation method like exact-low-resolution-brain-electromagnetic-tomography (eLORETA), which calculates the power of cortical electrical activity from EEG data, has been widely used to investigate cortical electrical activity in neuropsychiatric diseases. However, the recently developed field of mathematics "information geometry" indicates that EEG has another dimension orthogonal to power dimension - that of normalized power variance (NPV). In addition, by introducing the idea of information geometry, a significantly faster convergent estimator of NPV was obtained. Research into this NPV coordinate has been limited thus far. In this study, we applied this NPV analysis of eLORETA to idiopathic normal pressure hydrocephalus (iNPH) patients prior to a cerebrospinal fluid (CSF) shunt operation, where traditional power analysis could not detect any difference associated with CSF shunt operation outcome. Our NPV analysis of eLORETA detected significantly higher NPV values at the high convexity area in the beta frequency band between 17 shunt responders and 19 non-responders. Considering our present and past research findings about NPV, we also discuss the advantage of this application of NPV representing a sensitive early warning signal of cortical impairment. Overall, our findings demonstrated that EEG has another dimension - that of NPV, which contains a lot of information about cortical electrical activity that can be useful in clinical practice.

归一化功率方差:EEG分析中一个与功率正交的新领域。
迄今为止,脑电图(EEG)已通过检查EEG波、识别异常放电和基波减慢,用于诊断癫痫、痴呆和意识障碍。此外,脑电图功率分析与精确低分辨率脑电磁断层扫描(eLORETA)等源估计方法相结合,从脑电图数据中计算皮层电活动的功率,已被广泛用于研究神经精神疾病中的皮层电活动。然而,最近发展起来的数学领域“信息几何”表明,EEG有另一个与功率维度正交的维度——归一化功率方差(NPV)。此外,通过引入信息几何的思想,获得了一个收敛速度明显更快的NPV估计量。到目前为止,对这种NPV坐标的研究是有限的。在本研究中,我们将eLORETA的NPV分析应用于脑脊液(CSF)分流手术前的特发性常压脑积水(iNPH)患者,其中传统的功率分析无法检测到与脑脊液分流手术结果相关的任何差异。我们对eLORETA的NPV分析发现,在17名分流应答者和19名无应答者之间,β频带的高凸性区域的NPV值显著更高。考虑到我们目前和过去关于NPV的研究结果,我们还讨论了NPV作为皮层损伤的敏感预警信号的应用优势。总的来说,我们的研究结果表明,EEG还有另一个维度——NPV,它包含了许多关于皮层电活动的信息,这些信息在临床实践中是有用的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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