Fundamental Study on Non-stationary Detection Method of Long Biological Time Series Using Topological Mappings

基于拓扑映射的长生物时间序列非平稳检测方法基础研究

基本信息

批准号：
03452183
负责人：
SAITO Masao
金额：
$ 2.43万
依托单位：
University of Tokyo
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (B)
财政年份：
1991
资助国家：
日本
起止时间：
1991 至 1992
项目状态：
已结题

项目摘要

We made fundamental consideration on the method of utilizing topological mappings(TM),explored by T.Kohonen,in order to detect non-stationary in long biological time series. We obtained results below.1.We analyzed properties of TM theoretically by treating TM as a kind of adaptive vector quantization algorithm. We derived quantitatively the relationship between the reference vector distribution and the probability distribution of input signal,by constructing Lyapunov function for TM.2.Property of TM,called as automatic selection of characteristic dimension,can be used to extract intrinsic structure of input. Placement of reference vectors generated by TM is little affected by choice of coordinate system in input space,and this property of TM is plausible for the application of TM to signal analysis.3.We proposed the method of detecting non-stationary in long biological time series,which makes use of the placement of reference vectors generated by TM and continuated mappings constructed by it.4.The proposed method was applied,as an example,to the analysis of sleep EEG. It was confirmed that obtained results was in good agreement with knowledge in clinical fields.5.As the amount of calculation is so large that it may cause problem in applying this method,we made an idea for reducing the amount of calculation. This idea can actually reduce the amount of calculation to large extent.On the basis of results above,we plan to develop methods in which we utilize metric information in continuated mappings,and to apply this method to various cases and to gather larger amount of data, in order to make closer comparison with clinical knowledge and to improve reliability of the detection.

我们对T.Kohonen探索的利用拓扑映射（TM）的方法进行了基本考虑，以检测长生物学时间序列中的非平稳性。 1.我们通过将TM视为一种自适应矢量量化算法，从理论上分析了TM的性质。我们通过构造TM的Lyapunov函数（称为自动选择特征性维度的自动选择）来定量地得出参考矢量分布和输入信号的概率分布之间的关系，可用于提取输入的内在结构。 TM产生的参考矢量的放置很少受到输入空间中的坐标系统的选择，并且TM的这种属性对于TM在信号分析中的应用是合理的。3。我们提出了在长期生物学时间序列中检测非平稳的方法，这可以使TM和Contented Mapping appers insept insepty seption insepty.4. the It the the the the the It the It ththe-aptied.4.4.据证实，获得的结果与临床领域的知识非常吻合。5。计算量如此之大，以至于在应用此方法时可能会引起问题，我们提出了减少计算量的想法。这个想法实际上可以在很大程度上减少计算量。在上述结果的基础上，我们计划开发我们在持续映射中利用度量信息的方法，并将这种方法应用于各种情况并收集大量数据，以便与临床知识进行更紧密的比较，并提高检测的可靠性。