Cerutti S, Liberati D, Avanzini G, Franceschetti S, Panzica F
J Biomed Eng. 1986 Jul;8(3):244-54. doi: 10.1016/0141-5425(86)90091-9.
A procedure is described which aims to classify an EEG recorded during neurosurgery, for example intracerebral aneurysm clipping. A parametric approach is used; it employs auto-regressive (AR) modelling and Kalman filtering to quantify directly the dynamics of the EEG generating mechanism, supposing it to be a linear, time-invariant system driven by white noise. The results of this EEG processing are analysed together with simultaneous values of arterial blood pressure (ABP) as surgery of this kind is carried out under conditions of controlled hypotension. The object is to compare the sensitivity of ABP data with that obtained from the EEG and so provide an early warning of a potentially dangerous non-physiological state induced by the hypotensive drug (in this case sodium nitroprusside). Some methodological comments on the correct implementation of these algorithms are given and the procedure is compared with similar approaches which have appeared in the literature during the last few years. Particular emphasis is placed on the power spectral analysis of the signal by pointing out a method for spectral decomposition, related to AR power density estimation, which permits the separation of single spectral components in terms of central frequencies and their associated power. Other potential applications of this method are in long term EEG monitoring for the detection of changes due for example to drug infusion, to fast transient events, or to changes in the stationary condition.
本文描述了一种程序，旨在对神经外科手术期间记录的脑电图进行分类，例如脑内动脉瘤夹闭手术期间的脑电图。该程序采用参数化方法，运用自回归（AR）建模和卡尔曼滤波直接量化脑电图产生机制的动态变化，假设其为一个由白噪声驱动的线性、时不变系统。在控制性低血压条件下进行此类手术时，将脑电图处理结果与动脉血压（ABP）的同步值进行分析。目的是比较ABP数据与脑电图数据的敏感性，从而为降压药物（在此例中为硝普钠）诱发的潜在危险非生理状态提供早期预警。文中给出了关于这些算法正确实施的一些方法学评论，并将该程序与过去几年文献中出现的类似方法进行了比较。特别强调了通过指出一种与AR功率密度估计相关的频谱分解方法对信号进行功率谱分析，该方法允许根据中心频率及其相关功率分离单个频谱成分。此方法的其他潜在应用包括长期脑电图监测，以检测例如药物输注、快速瞬态事件或稳态变化等引起的变化。
