Isaksson A, Wennberg A
Electroencephalogr Clin Neurophysiol. 1975 Jan;38(1):79-86. doi: 10.1016/0013-4694(75)90212-6.
Spectral parameter analysis (SPA) of the EEG provides a description of the distribution of spectral power in the EEG signal in the form of a rational spectrum with not more than 8 parameters. The spectrum is divided into 1-3 components described by frequency and power parameters: bandwidth (delta), peak frequency (f) and power (G). These spectral parameters are determined with the aid of a computer. The character of the EEG signal decides whether 1, 2 or 3 components (delta, alpha, beta) are needed to describe the spectrum. To test its practical value, the result of SPA was compared with that of ordinary visual evaluation of the EEG of 65 healthy men between the ages of 18 and 22 years. 20 sec sections from different leads were analysed and evaluated visually. Each EEG section was graded according to the amount of visually evaluated slow activity (VESA). To investigate the relation between the degree of VESA and the SPA result, statistical calculations (variance and regression analyses) were carried out, both for single SPA parameters and for the general type of spectrum, i.e., the number of components composing the spectrum. The SPA results from sections with artefacts were treated separately and compared statistically with results from artefact-free sections. In records with a high degree of VESA, all the leads analysed showed a tendency to have a power spectrum of low order, i.e., with few components. In most leads there was a linear regression between the degree of VESA and the bandwidth and power of the delta and alpha components. In several cases this relation was an expression of a significant linear change of the SPA parameter as a function of the degree of VESA. On the other hand the parameters of the beta component showed no relation to the degree of VESA. It was found that muscle activity could influence any spectral component thereby providing it with a strongly increased bandwidth. This is probably due to the fact that muscle activity resembles white noise in this particular frequency range. Low frequency artefacts affected only the delta component the bandwidth of which was significantly smaller than in the artefact-free sections.
脑电图的频谱参数分析(SPA)以不超过8个参数的有理谱形式描述了脑电图信号中的频谱功率分布。频谱被分为1至3个由频率和功率参数描述的成分:带宽(δ)、峰值频率(f)和功率(G)。这些频谱参数借助计算机确定。脑电图信号的特征决定了描述频谱需要1个、2个还是3个成分(δ、α、β)。为测试其实际价值,将65名年龄在18至22岁之间的健康男性的SPA结果与脑电图的普通视觉评估结果进行了比较。对来自不同导联的20秒片段进行了视觉分析和评估。每个脑电图片段根据视觉评估的慢波活动量(VESA)进行分级。为研究VESA程度与SPA结果之间的关系,对单个SPA参数以及频谱的一般类型(即构成频谱的成分数量)进行了统计计算(方差分析和回归分析)。对存在伪迹的片段的SPA结果进行单独处理,并与无伪迹片段的结果进行统计学比较。在VESA程度较高的记录中,所有分析的导联都显示出具有低阶功率谱的趋势,即成分较少。在大多数导联中,VESA程度与δ和α成分的带宽及功率之间存在线性回归。在一些情况下,这种关系表现为SPA参数随VESA程度的显著线性变化。另一方面,β成分的参数与VESA程度无关。发现肌肉活动可影响任何频谱成分,从而使其带宽大幅增加。这可能是因为在这个特定频率范围内肌肉活动类似于白噪声。低频伪迹仅影响δ成分,其带宽明显小于无伪迹片段。