Hemakom Apit, Powezka Katarzyna, Goverdovsky Valentin, Jaffer Usman, Mandic Danilo P
Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK.
Department of Vascular Surgery, Imperial College London, London SW7 2AZ, UK.
R Soc Open Sci. 2017 Nov 6;4(12):170853. doi: 10.1098/rsos.170853. eCollection 2017 Dec.
A highly localized data-association measure, termed intrinsic synchrosqueezing transform (ISC), is proposed for the analysis of coupled nonlinear and non-stationary multivariate signals. This is achieved based on a combination of noise-assisted multivariate empirical mode decomposition and short-time Fourier transform-based univariate and multivariate synchrosqueezing transforms. It is shown that the ISC outperforms six other combinations of algorithms in estimating degrees of synchrony in synthetic linear and nonlinear bivariate signals. Its advantage is further illustrated in the precise identification of the synchronized respiratory and heart rate variability frequencies among a subset of bass singers of a professional choir, where it distinctly exhibits better performance than the continuous wavelet transform-based ISC. We also introduce an extension to the intrinsic phase synchrony (IPS) measure, referred to as nested intrinsic phase synchrony (N-IPS), for the empirical quantification of physically meaningful and straightforward-to-interpret trends in phase synchrony. The N-IPS is employed to reveal physically meaningful variations in the levels of cooperation in choir singing and performing a surgical procedure. Both the proposed techniques successfully reveal degrees of synchronization of the physiological signals in two different aspects: (i) precise localization of synchrony in time and frequency (ISC), and (ii) large-scale analysis for the empirical quantification of physically meaningful trends in synchrony (N-IPS).
本文提出了一种高度局部化的数据关联度量方法，称为本征同步挤压变换（ISC），用于分析耦合的非线性和非平稳多变量信号。这是基于噪声辅助多变量经验模式分解与基于短时傅里叶变换的单变量和多变量同步挤压变换相结合来实现的。结果表明，在估计合成线性和非线性双变量信号的同步程度方面，ISC优于其他六种算法组合。在一个专业合唱团的部分低音歌手同步呼吸和心率变异性频率的精确识别中，进一步说明了其优势，在该应用中，它明显表现出比基于连续小波变换的ISC更好的性能。我们还引入了本征相位同步（IPS）度量的扩展，称为嵌套本征相位同步（N-IPS），用于对相位同步中具有物理意义且易于解释的趋势进行经验量化。N-IPS用于揭示合唱演唱和外科手术过程中合作水平的具有物理意义的变化。所提出的两种技术都成功地从两个不同方面揭示了生理信号的同步程度：（i）同步在时间和频率上的精确定位（ISC），以及（ii）对同步中具有物理意义的趋势进行经验量化的大规模分析（N-IPS）。
