KurSL: Model of anharmonic coupled oscillations based on Kuramoto coupling and Sturm-Liouville problem

Journal article


Cadenas, O, Laszuk, D and Slawomir, N (2018). KurSL: Model of anharmonic coupled oscillations based on Kuramoto coupling and Sturm-Liouville problem. Advances in Data Science and Adaptive Analysis. 10 (02). https://doi.org/10.1142/S2424922X18400028
AuthorsCadenas, O, Laszuk, D and Slawomir, N
Abstract

Physiological signalling is often oscillatory and shows nonlinearity due to complex interactions of underlying processes or signal propagation delays. This is particularly evident in case of brain activity which is subject to various feedback loop interactions between di erent brain structures, that coordinate their activity to support normal function. In order to understand such signalling in health and disease, methods are needed that can deal with such complex oscillatory phenomena. In this paper, a data-driven method for analysing anharmonic oscillations is introduced. The KurSL model incorporates two well-studied components, which in the past have been used separately to analyse oscillatory behaviour. The Sturm-Liouville equations describe a form of a general oscillation, and the Kuramoto coupling model represents a set of oscillators interacting in the phase domain. Integration of these components provides a flexible framework for capturing complex interactions of oscillatory processes of more general form than the most commonly used harmonic oscillators. The paper introduces a mathematical framework of the KurSL model and analyses its behaviour for a variety of parameter ranges. The signi cance of the model follows from its ability to provide information about coupled oscillators' phase dynamics directly from the time series. KurSL o ers a novel framework for analysing a wide range of complex oscillatory behaviours, such as encountered in physiological signals.

KeywordsKuramoto; Sturm-Liouville; Oscillation
Year2018
JournalAdvances in Data Science and Adaptive Analysis
Journal citation10 (02)
PublisherWorld Scientific Publishing
ISSN2424-922X
Digital Object Identifier (DOI)https://doi.org/10.1142/S2424922X18400028
Publication dates
Print09 May 2018
Publication process dates
Deposited10 May 2018
Accepted11 Mar 2018
Accepted author manuscript
License
File Access Level
Open
Permalink -

https://openresearch.lsbu.ac.uk/item/86qyy

Download files


Accepted author manuscript
kursl-model-anharmonic.pdf
License: CC BY 4.0
File access level: Open

  • 290
    total views
  • 130
    total downloads
  • 10
    views this month
  • 0
    downloads this month

Export as

Related outputs

Preprocessing 2D data for fast convex hull computations
Cadenas, O and Megson, GM (2019). Preprocessing 2D data for fast convex hull computations. PLoS ONE. 14 (2), p. e0212189. https://doi.org/10.1371/journal.pone.0212189
Running Median Algorithm and Implementation for Integer Streaming Applications
Cadenas, O and Megson, GM (2018). Running Median Algorithm and Implementation for Integer Streaming Applications. IEEE Embedded Systems Letters. 11 (2), pp. 58-61. https://doi.org/10.1109/LES.2018.2868409
Rapid preconditioning of data for accelerating convex hull algorithms
Cadenas, O and Megson, G (2014). Rapid preconditioning of data for accelerating convex hull algorithms. Electronics Letters. 50 (4), pp. 270-272. https://doi.org/10.1049/el.2013.3507
Virtualization for cost-effective teaching of assembly language
Cadenas, O, Sherratt, S, Howlett, D, Guy, C and Lundqvist, K (2015). Virtualization for cost-effective teaching of assembly language. IEEE Transactions on Education. 58 (4), pp. 282-288. https://doi.org/10.1109/TE.2015.2405895
Median architecture by accumulative parallel counters
Cadenas, O, Megson, G and Sherratt, S (2015). Median architecture by accumulative parallel counters. IEEE Transactions on Circuits and Systems II: Express Briefs. 62 (7), pp. 661-665. https://doi.org/10.1109/TCSII.2015.2415655
Pipelined median architecture
Cadenas, O (2015). Pipelined median architecture. Electronics Letters. 51 (24), pp. 1999-2001. https://doi.org/10.1049/el.2015.1898
Preconditioning 2D integer data for fast convex hull computations
Cadenas, O, Megson, G.M. and Luengo Hendriks, C.L. (2016). Preconditioning 2D integer data for fast convex hull computations. PLoS ONE. 11 (3). https://doi.org/10.1371/journal.pone.0149860
EMD performance comparison: single vs double floating points
Laszuk, D, Cadenas, O. and Nasuto, J (2016). EMD performance comparison: single vs double floating points. International journal of signal processing systems. 4 (4), pp. 349-353. https://doi.org/10.18178/ijsps.4.4.349-353
On the Phase Coupling of Two Components Mixing in Empirical Mode Decomposition
Laszuk, D, Cadenas, O. and Nasuto, Slawomir J. (2016). On the Phase Coupling of Two Components Mixing in Empirical Mode Decomposition. Advances in Data Science and Adaptive Analysis. 8 (1). https://doi.org/10.1142/S2424922X16500042