Indian Institute of Technology, Roorkee, India.
Physiol Meas. 2019 Nov 4;40(10):105006. doi: 10.1088/1361-6579/ab499e.
Information entropy is generally employed for analysing the complexity of physiological signals. However, most definitions of entropy estimate the degree of compressibility and thus quantify the randomness. Physiological signals are very complex because of nonlinear relationships and interactions between various systems and subsystems of the body. Therefore, analysis of randomness may not be sufficient to describe this complexity. To analyse the complexity of heart rate variability (HRV), a new entropy method, phase entropy (PhEn), has been proposed as a quantification of two-dimensional phase space.
The second-order difference plot (SODP), a two-dimensional phase space, provides a visual summary of the rate of variability. The distribution of scatter points in a SODP provides information about the dynamics of the underlying system. PhEn estimates the Shannon entropy of the weighted distribution in a coarse-grained SODP.
The performance of PhEn has been evaluated using simulated signals, synthetic HRV signals and real HRV signals. PhEn shows a better discriminating power and stability than other entropy measures. It is computationally efficient. Moreover, it has the ability to assess temporal asymmetry of physiological signals.
PhEn quantifies the multiplicity and rate of variability associated with physiological signals. It is sensitive to time irreversibility. Therefore, it appears to be a promising tool for analysing physiological signals such as HRV.
信息熵通常用于分析生理信号的复杂性。然而，大多数熵的定义估计压缩程度，从而量化随机性。由于身体各系统和子系统之间的非线性关系和相互作用，生理信号非常复杂。因此，随机性分析可能不足以描述这种复杂性。为了分析心率变异性（HRV）的复杂性，提出了一种新的熵方法——相位熵（PhEn），作为二维相空间的量化方法。
二阶差分图（SODP）是一个二维相空间，提供了对变异性速率的直观总结。SODP 中散点的分布提供了有关基础系统动态的信息。PhEn 估计粗粒度 SODP 中加权分布的香农熵。
使用模拟信号、合成 HRV 信号和真实 HRV 信号评估了 PhEn 的性能。PhEn 显示出比其他熵度量更好的区分能力和稳定性。它计算效率高。此外，它具有评估生理信号时间不对称性的能力。
PhEn 量化了与生理信号相关的多重性和变异性速率。它对时间不可逆性敏感。因此，它似乎是分析 HRV 等生理信号的有前途的工具。
