期刊
ROYAL SOCIETY OPEN SCIENCE
卷 4, 期 12, 页码 -出版社
ROYAL SOC
DOI: 10.1098/rsos.170853
关键词
multivariate empirical mode decomposition; multivariate synchrosqueezing transform; intrinsic multi-scale analysis; coherence; respiration; heart rate variability
资金
- Royal Thai Government
- Multidisciplinary University Research Initiative/Engineering and Physical sciences Research Council [EP/P008461]
- EPSRC Pathways to Impact [PS8038]
- EPSRC [EP/P009204/1, EP/P008461/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/P008461/1] Funding Source: researchfish
- Rosetrees Trust [M390] Funding Source: researchfish
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).
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