Table of Contents

• MODA
  • Purpose
  • Getting started
• References
• Example applications

MODA

MODA (Multiscale Oscillatory Dynamics Analysis) is a numerical toolbox developed by the Nonlinear & Biomedical Physics group at Lancaster University and the Nonlinear Dynamics and Synergetic Group at the Faculty of Electrical Engineering, University of Ljubljana, Slovenia under the supervision of Aneta Stefanovska.

Note: A Python implementation of MODA, PyMODA, is currently in development. PyMODA does not require a MATLAB license.

Purpose

MODA is designed for analysing real-life time-series that are assumed to be the output of some a priori unknown non-autonomous dynamical system, and deriving important properties about this dynamical system from the time-series.

MODA includes methods both for analysing the recordings of a single signal over time, and for analysing a set of recordings of multiple different signals over time. In particular, it has tools for analysing bivariate time-series consisting of the simultaneous recordings of two different signals over time, with a view to examining possible connections between the two signals.

Getting started

To get started, please see the User Guide.

References

Overview

1. J Newman, G Lancaster and A Stefanovska, “Multiscale Oscillatory Dynamics Analysis”, v1.01, User Manual, 2018.
2. P Clemson, G Lancaster, A Stefanovska, “Reconstructing time-dependent dynamics”, Proc IEEE 104, 223–241 (2016).
3. P Clemson, A Stefanovska, “Discerning non-autonomous dynamics”, Phys Rep 542, 297-368 (2014).

Time-Frequency Analysis

1. D Iatsenko, P V E McClintock, A Stefanovska, “Linear and synchrosqueezed time-frequency representations revisited: Overview, standards of use, resolution, reconstruction, concentration, and algorithms”, Dig Sig Proc 42, 1–26 (2015).
2. P Clemson, G Lancaster, A Stefanovska, “Reconstructing time-dependent dynamics”, Proc IEEE 104, 223–241 (2016).
3. G Lancaster, D Iatsenko, A Pidde, V Ticcinelli, A Stefanovska, “Surrogate data for hypothesis testing of physical systems”, Phys Rep 748, 1–60 (2018).

Wavelet Phase Coherence

1. Bandrivskyy A, Bernjak A, McClintock P V E, Stefanovska A, “Wavelet phase coherence analysis: Application to skin temperature and blood flow”, Cardiovasc Engin 4, 89–93 (2004).
2. Sheppard L W, Stefanovska A, McClintock P V E, “Testing for time-localised coherence in bivariate data”, Phys. Rev. E 85, 046205 (2012).

Ridge Extraction & Filtering

1. D Iatsenko, P V E McClintock, A Stefanovska, “Nonlinear mode decomposition: A noise-robust, adaptive decomposition method”, Phys Rev E 92, 032916 (2015).
2. D Iatsenko, P V E McClintock, A Stefanovska, “Extraction of instantaneous frequencies from ridges in time-frequency representations of signals”, Sig Process 125, 290–303 (2016).

Wavelet Bispectrum Analysis

1. J Jamšek, A Stefanovska, P V E McClintock, “Wavelet bispectral analysis for the study of interactions among oscillators whose basic frequencies are significantly time variable”, Phys Rev E 76, 046221 (2007).
2. J Jamšek, M Paluš, A Stefanovska, “Detecting couplings between interacting oscillators with time-varying basic frequencies: Instantaneous wavelet bispectrum and information theoretic approach”, Phys Rev E 81, 036207 (2010).
3. J Newman, A Pidde, A Stefanovska, “Defining the wavelet bispectrum”, submitted (2019).

Dynamical Bayesian Inference

1. V N Smelyanskiy, D G Luchinsky, A Stefanovska, P V E McClintock, “Inference of a nonlinear stochastic model of the cardiorespiratory interaction”, Phys Rev Lett 94, 098101 (2005).
2. T Stankovski, A Duggento, P V E McClintock, A Stefanovska, “Inference of time-evolving coupled dynamical systems in the presence of noise”, Phys Rev Lett 109, 024101 (2012).
3. T Stankovski, A Duggento, P V E McClintock, A Stefanovska, “A tutorial on time-evolving dynamical Bayesian inference”, Eur Phys J – Special Topics 223, 2685-2703 (2014).
4. T Stankovski, T Pereira, P V E McClintock, A Stefanovska, “Coupling functions: Universal insights into dynamical interaction mechanisms”, Rev Mod Phys 89, 045001 (2017).
5. Special issue of the Philos Trans Royal Soc A (2019) with contributions by Kuramoto and others.

Example applications

Wavelet Phase Coherence

1. Sheppard L W, Vuksanović V, McClintock P V E, Stefanovska A, Oscillatory dynamics of vasoconstriction and vasodilation identified by time-localized phase coherence Phys Med Biol 56, 3583–3601 (2011).
2. A Bernjak, J Cui, S Iwase, T Mano, A Stefanovska, D L Eckberg, “Human sympathetic outflows to skin and muscle target organs fluctuate concordantly over a wide range of time-varying frequencies”, J Physiol 590, 363–375 (2012).
3. P Kvandal, L Sheppard, S A Landsverk, A Stefanovska, K A Kirkebøen, “Impaired cerebrovascular reactivity after acute traumatic brain injury can be detected by wavelet phase coherence analysis of the intracranial and arterial blood pressure signals”, J Clin Monit Comput 27, 375-383 (2013).

Ridge Extraction & Filtering

1. D Iatsenko, A Bernjak, T Stankovski, Y Shiogai, P J Owen-Lynch, P B M Clarkson, P V E McClintock, A Stefanovska, “Evolution of cardiorespiratory interactions with age”, Phil Trans R Soc A 371, 20110622 (2013).
2. V Ticcinelli, T Stankovski, D Iatsenko, A Bernjak, A E Bradbury, A R Gallagher, P B M Clarkson, P V E McClintock, A Stefanovska, “Coherence and coupling functions reveal microvascular impairment in treated hypertension”, Front Physiol 8, 749 (2017).
3. YA Abdulhameed, G Lancaster, PVE McClintock, A Stefanovska, “On the suitability of laser-Doppler flowmetry for capturing microvascular blood flow dynamics from darkly pigmented skin”, Physiol Meas, 40, 074005 (2019).

Wavelet Bispectrum Analysis

1. J Jamšek, A Stefanovska, P V E McClintock, “Nonlinear cardio-respiratory interactions revealed by time-phase bispectral analysis”, Phys Medicine Biol 49, 4407 (2004).

Dynamical Bayesian Inference

1. B Musizza, A Stefanovska, P V E McClintock, M Paluš, J Petrovčič, S Ribarič, F F Bajrović, “Interactions between cardiac, respiratory and EEG-delta oscillations in rats during anaesthesia”, J Physiol 580 315–326 (2007).
2. T Stankovski, V Ticcinelli, P V E McClintock, A Stefanovska, “Coupling functions in networks of oscillators”, New J Phys 17, 035002 (2015).
3. T Stankovski, S Petkoski, J Ræder, A F Smith, P V E McClintock, A Stefanovska, “Alterations in the coupling functions between cortical and cardio-respiratory oscillations due to anaesthesia with propofol and sevoflurane”, Philos Trans Royal Soc A 374, 20150186 (2016).
4. V Ticcinelli, T Stankovski, D Iatsenko, A Bernjak, A E Bradbury, A R Gallagher, P B M Clarkson, P V E McClintock, A Stefanovska, “Coherence and coupling functions reveal microvascular impairment in treated hypertension”, Front Physiol 8, 749 (2017).