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Dmytro Iatsenko
Researcher at Lancaster University
Publications - 24
Citations - 1138
Dmytro Iatsenko is an academic researcher from Lancaster University. The author has contributed to research in topics: Wavelet transform & Kuramoto model. The author has an hindex of 13, co-authored 24 publications receiving 875 citations. Previous affiliations of Dmytro Iatsenko include Deutsche Bank.
Papers
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Journal ArticleDOI
Surrogate data for hypothesis testing of physical systems
Gemma Lancaster,Dmytro Iatsenko,Aleksandra Pidde,Aleksandra Pidde,Valentina Ticcinelli,Aneta Stefanovska +5 more
TL;DR: A detailed overview of a wide range of surrogate types is provided, which include Fourier transform based surrogates, which have since been developed to test increasingly varied null hypotheses while characterizing the dynamics of complex systems, including uncorrelated and correlated noise, coupling between systems, and synchronization.
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Linear and synchrosqueezed time-frequency representations revisited
TL;DR: It is shown that the higher concentration of the synchrosqueezed transforms does not seem to imply better resolution properties, so that the SWFT and SWT do not appear to provide any significant advantages over the original WFT and WT apart from a more visually appealing pictures.
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Extraction of instantaneous frequencies from ridges in time-frequency representations of signals
TL;DR: In this article, the authors proposed a method based on dynamic path optimization and fixed point iteration to find an appropriate ridge curve: a sequence of amplitude peak positions (ridge points), corresponding to the component of interest and providing a measure of its instantaneous frequency.
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Evolution of cardiorespiratory interactions with age
Dmytro Iatsenko,Alan Bernjak,Alan Bernjak,Tomislav Stankovski,Y. Shiogai,P. J. Owen-Lynch,Peter Clarkson,Peter V. E. McClintock,Aneta Stefanovska,Aneta Stefanovska +9 more
TL;DR: It is shown that the direct and indirect respiratory modulations of the heart rate both decrease with age, and that the cardiorespiratory coupling becomes less stable and more time-variable.
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Nonlinear mode decomposition: a noise-robust, adaptive decomposition method.
TL;DR: Nonlinear mode decomposition (NMD) as discussed by the authors decomposes a given signal into a set of physically meaningful oscillations for any wave form, simultaneously removing the noise, based on the powerful combination of time-frequency analysis techniques, together with the adaptive choice of their parameters.