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).