Evaluation of interaction dynamics of concurrent processes

Open access

Abstract

The purpose of this paper is to present the wavelet tools that enable the detection of temporal interactions of concurrent processes. In particular, the determination of interaction coherence of time-varying signals is achieved using a complex continuous wavelet transform. This paper has used electrocardiogram (ECG) and seismocardiogram (SCG) data set to show multiple continuous wavelet analysis techniques based on Morlet wavelet transform. MATLAB Graphical User Interface (GUI), developed in the reported research to assist in quick and simple data analysis, is presented. These software tools can discover the interaction dynamics of time-varying signals, hence they can reveal their correlation in phase and amplitude, as well as their non-linear interconnections. The user-friendly MATLAB GUI enables effective use of the developed software what enables to load two processes under investigation, make choice of the required processing parameters, and then perform the analysis. The software developed is a useful tool for researchers who have a need for investigation of interaction dynamics of concurrent processes.

[1] P. S. Addison, J. N. Watson, G. R. Clegg, P. A. Steen and C. E. Robertson, ”Finding Coordinated Atrial Activity During Ventricular Fibrillation Using Wavelet Decomposition”, Analyzing Surface ECGs with A New Signal Analysis Technique to Better Understand Sudden Cardiac Death”, IEEE Trans.Engineering Medicine and Biology, 2002.

[2] N. D. Kelley, R. M. Osgood, J. T. Bialasiewicz and A. Jakubowski, ”Using Wavelet Analysis to Assess Turbulence/ Rotor Interactions”, Wind Energy, vol.3, no. 3, pp. 121-134, 2001.

[3] N. D. Kelley, B. J. Jonkman, J. T. Bialasiewicz, G. N. Scott and L. S. Redmond, ”The Impact of Coherent Turbulence on Wind Turbine Aeroelastic Response and Its Simulation”, Proc. AWEA Windpower ’05, Denver, 2005.

[4] D. Gonzlez, J. T. Bialasiewicz, J. Balcells, and J. Gago, ”Wavelet- Based Performance Evaluation of Power Converters Operating With Modulated Switching Frequency”, IEEE Trans”, Ind. Electron., vol. 55, no.8, pp. 3167-3176, August 2008.

[5] J. T. Bialasiewicz, D. Gonzlez, J. Gago, and J. Balcells, ”Wavelet- Based Approach to Evaluation of Signal Integrity”, IEEE Trans. Ind. Electron., vol. 60, no.10, pp. 4590-4598, October 2013.

[6] J. T. Bialasiewicz, ”Application of Wavelet Scalogram and Coscalogram for Analysis of Biomedical Signals”, Proceedings of the 2nd International Conference on Biomedical Engineering and Systems, Barcelona, Spain, July 2015, paper no. 333.

[7] M. A. García-González, A. Argelagós-Palau,M. Fernndez-Chimeno and J. Ramos-Castro, ”A comparison of heartbeat detectors for the seismocardiogram”, ” Computing Cardiology Conference (CinC), 2013.

[8] M. A. García-González, A. Argelagós-Palau,M. Fernndez-Chimeno and J. Ramos-Castro, ”Differences QRS Locations due to ECG Lead: Relationship with Breathing”, XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013, IFMBE Proceedings vol. 41, pp. 962-964, 2014.

[9] J. C. Grinsted, J. C. Moore and S. Jevreja, ”Applications of the cross wavelet transform and wavelet coherence to geophysical time series”, Nonlinear Processes Geophysics 2004, 11:561-566.

[10] Mohamed A. K. Elsayed, ”Wavlet bicoherence analysis of wind-wave interaction”, Ocean Engineering 33, 2006, pp. 458-470.

[11] J. P. Lachaux, L. A. Rudrauf, A. Lutz, D. Cosmelli, M. L. V. Quyen and Martinerie J. Varela, ”Estimating the time-course of coherence between single-trial brain signals: An introduction to wavelet coherence”, Clin Neurophysiol 2002; 32:157-74.

[12] H. R. M. Wolfgang, B. Hristoph, A. Matthias, W. Herbert and T. Edward, ”Coherence of gamma-band EEG activity as a basis for associative learning”, Nature 1999,397:43-46.

[13] E. Sanchez, T. Estrada, C. Hidalgo, B. Branas, B. Carreras and L. Garcia, ”Wavelet bicoherence: a new turbulence analysis tool”, Phys. Plasmas, 1995;2:301732.

[14] Xiaoli Li, Xin Yao, J. Fox and J. G. Je erys, ”Interaction dynamics of neuronal oscillations analysed using wavelet transforms”, J. Neurosci. Methods, 2007, 160(1):178-185.

[15] A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. Ch. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng and H. E. Stanley, ”PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals”, ” Circulation 101, no.23, 2000.

Journal of Electrical Engineering

The Journal of Slovak University of Technology

Journal Information


IMPACT FACTOR 2017: 0.508
5-year IMPACT FACTOR: 0.549

CiteScore 2017: 0.78

SCImago Journal Rank (SJR) 2017: 0.205
Source Normalized Impact per Paper (SNIP) 2017: 0.506

Metrics

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 101 101 3
PDF Downloads 42 42 1