Bender R, Schultz B, Schultz A, Pichlmayr I
Department of Anesthesiology, Hannover Medical School, Federal Republic of Germany.
Biomed Tech (Berl). 1991 Oct;36(10):236-40. doi: 10.1515/bmte.1991.36.10.236.
In EEG analysis an automatic pattern recognition is of interest. In this paper the usefulness of autoregressive parameters to classify EEG segments recorded during anesthesia is examined. Assuming that the AR parameters are multivariate normally distributed, parametric methods of discriminant analysis can be applied. The results show that AR parameters have high discriminating power and that the lowest error classification rate (smaller than 3%) is obtained by using quadratic discriminant functions. Consequently autoregressive parameters are efficient for classifying EEG segments into general stages of anesthesia.
在脑电图分析中，自动模式识别很受关注。本文研究了自回归参数对麻醉期间记录的脑电图片段进行分类的有效性。假设自回归参数呈多元正态分布，则可应用判别分析的参数方法。结果表明，自回归参数具有较高的判别能力，使用二次判别函数可获得最低的错误分类率（小于3%）。因此，自回归参数对于将脑电图片段分类为麻醉的一般阶段是有效的。
