Data from: Quantifying team cooperation through intrinsic multi-scale measures: respiratory and cardiac synchronisation in choir singers and surgical teams

A highly localised data-association measure, termed intrinsic synchrosqueezing transform (ISC), is proposed for the analysis of coupled nonlinear and nonstationary multivariate signals. This is achieved based on a combination of noise-assisted multivariate empirical mode decomposition (NA-MEMD) and short-time Fourier transform (STFT)-based univariate and multivariate synchrosqueezing transforms (FSST and F-MSST). 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 synchronised respiratory and HRV frequencies among a subset of bass singers of a professional choir, where it distinctly exhibits better performance than the continuous wavelet transform (CWT)-based ISC. We also introduce an extension to 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 synchronisation of the physiological signals in two different aspects: (i) precise localisation of synchrony in time and frequency (ISC), and (ii) large scale analysis for the empirical quantification of physically meaningful trends in synchrony (N-IPS).本文提出一种高局域化的数据关联测度——固有同步挤压变换（intrinsic synchrosqueezing transform, ISC），用于耦合非线性非平稳多变量信号的分析。该方法基于噪声辅助多变量经验模态分解（noise-assisted multivariate empirical mode decomposition, NA-MEMD）与基于短时傅里叶变换（short-time Fourier transform, STFT）的单变量及多变量同步挤压变换（FSST与F-MSST）的结合实现。研究表明，在合成线性与非线性双变量信号的同步程度估计任务中，ISC的性能优于六种其他算法组合。针对某专业合唱团男低音歌手子集的同步呼吸频率与心率变异性（HRV）频率的精准识别场景，ISC的优势进一步得到验证，其表现显著优于基于连续小波变换（continuous wavelet transform, CWT）的ISC。此外，本文还提出了固有相位同步性（intrinsic phase synchrony, IPS）测度的扩展形式——嵌套固有相位同步性（nested intrinsic phase synchrony, N-IPS），用于经验层面量化相位同步性中具备物理意义且易于解读的趋势。N-IPS被用于揭示合唱团演唱与外科手术操作场景中协作水平的具有物理意义的变化。上述两种所提技术均成功从两个不同维度揭示了生理信号的同步程度：（i）时间与频率域内同步性的精准定位（ISC）；（ii）用于经验量化同步性中具备物理意义趋势的大规模分析（N-IPS）。