分层多变量多尺度离散熵用于生理信号分析
Stratified Multivariate Multiscale Dispersion Entropy for Physiological Signal Analysis
摘要：
多变量熵量化算法正在成为从多通道生理时间序列中提取信息的重要工具。然而，在分析来自异质器官系统的生理信号时，某些通道可能会掩盖其他通道的模式，导致信息丢失。在这里，我们引入了分层熵的框架，以优先考虑每个通道的动态基于其分配到各自的层，从而导致更丰富的描述的多通道时间序列。作为该框架的一个实现，介绍了分层多变量多尺度弥散熵的三种算法变体。这些变化和原始算法被应用于合成时间序列、波形生理时间序列和衍生生理数据。基于合成的时间序列实验，成功的变化优先级通道后，他们的阶层分配，同时保持低计算时间的原始算法。在波形生理时间序列和衍生生理数据的实验中，注意到当以原始算法为基准时，在变化中的多个层分配的辨别能力增加。这表明通过变化改善了生理状态监测。此外，我们的变化可以修改，利用先验知识的分层通道。因此，我们的研究提供了一种新的方法，以前无法访问的信息从多通道的时间序列从异构系统中获取。
英文摘要：
Multivariate entropy quantification algorithms are becoming a prominent tool for the extraction of information from multi-channel physiological time-series. However, in the analysis of physiological signals from heterogeneous organ systems, certain channels may overshadow the patterns of others, resulting in information loss. Here, we introduce the framework of Stratified Entropy to prioritize each channels' dynamics based on their allocation to respective strata, leading to a richer description of the multi-channel time-series. As an implementation of the framework, three algorithmic variations of the Stratified Multivariate Multiscale Dispersion Entropy are introduced. These variations and the original algorithm are applied to synthetic time-series, waveform physiological time-series, and derivative physiological data. Based on the synthetic time-series experiments, the variations successfully prioritize channels following their strata allocation while maintaining the low computation time of the original algorithm. In experiments on waveform physiological time-series and derivative physiological data, increased discrimination capacity was noted for multiple strata allocations in the variations when benchmarked to the original algorithm. This suggests improved physiological state monitoring by the variations. Furthermore, our variations can be modified to utilize a priori knowledge for the stratification of channels. Thus, our research provides a novel approach for the extraction of previously inaccessible information from multi-channel time series acquired from heterogeneous systems.
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