Abstract
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).
Highlights
Cooperative human activities require high degrees of mental and physical synchronization among multiple participants, to the extent that synchrony underpins performance level in activities, such as choir singing, playing music in an ensemble, rowing, flying an aeroplane with a co-pilot or performing surgical procedures
We propose an extension to the standard IPS, referred to as the nested intrinsic phase synchrony (N-IPS), which further decomposes time series of the degrees of synchrony between data channels obtained using the standard IPS into multiple scales of synchrony; only certain scales which admit meaningful physical interpretation are empirically combined, without any prior knowledge of the frequencies of such scales
We have introduced a data-association measure which exhibits high frequency and time localization, termed intrinsic synchrosqueezing transform (ISC), for the analysis of nonlinear and non-stationary multivariate signals
Summary
Cooperative human activities require high degrees of mental and physical synchronization among multiple participants, to the extent that synchrony underpins performance level in activities, such as choir singing, playing music in an ensemble, rowing, flying an aeroplane with a co-pilot or performing surgical procedures. When it comes to quantifying the degree of synchronization among participants, synchrony in physiological responses has been reported in respiration and heart rate variability (HRV) among the choir members [1,2]. The PNS, on the other hand, slows down physiological functions when the body is at rest
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