Lamothe R, Stroink G
Department of Physics, Dalhousie University, Halifax, Nova Scotia, Canada.
Med Biol Eng Comput. 1991 Sep;29(5):522-8. doi: 10.1007/BF02442325.
The applicability of orthogonal expansions (singular-value decomposition, Karhunen-Loève transform and principal-component analysis) for the purpose of identifying source distributions associated with definite electrophysiological events in the heart and brain is explored with a current dipole source model. By definition, the expansion eigenvectors are orthogonal, and as such will extract the features of one specific source only if all other secondary signals are orthogonal to that first source. The number of significant eigenvectors can be related to the number of original components forming a signal, but there is not a one-to-one correspondence between these eigenvectors and the individual components. Furthermore, many eigenvectors may be needed to faithfully represent even a single source, if that source is nonstationary. We conclude that generally it would be inappropriate to ascribe any physiological significance to the data resulting from such expansions.
利用电流偶极子源模型，探讨了正交展开（奇异值分解、卡尔胡宁－勒夫变换和主成分分析）在识别与心脏和大脑中特定电生理事件相关的源分布方面的适用性。根据定义，展开特征向量是正交的，因此只有在所有其他次要信号与第一个源正交时，它们才会提取一个特定源的特征。显著特征向量的数量可以与形成信号的原始成分数量相关，但这些特征向量与各个成分之间不存在一一对应关系。此外，如果源是非平稳的，可能需要许多特征向量才能如实地表示单个源。我们得出结论，一般而言，将此类展开产生的数据赋予任何生理意义都是不合适的。
