Features of Nonstationary Directional Coupling

Matlab code to generate the feature set used in the publication:

JM O'Toole, EM Dempsey, D Van Laere, Nonstationary coupling between heart rate and perfusion index in extremely preterm infants in the first day of life, Physiological Measurement, 2021. DOI:10.1088/1361-6579/abe3de

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Requirements | Examples | References | Contact

Requires

As Matlab code requires Matlab. Not tested with GNU Octave but may work.

Specific packages required:

1. The asymp_package_v3 toolbox which is available (http://www.lcs.poli.usp.br/~baccala/pdc/)
2. ARfit toolbox, available (https://github.com/tapios/arfit)

Download both packages and add to Matlab paths. See how to add path for more details.

Next, add the path for this project in Matlab; can do so by

>> add_path_here();

Main Functions

• Short-time information partial directed coherence (ST-iPDC): shorttime_iPDC.m
• Estimate features of the individual ST-iPDC: feats_IF_STiPDC.m
  • including Hjorth parameters: hjorth_feats.m
• Features for time-varying direction of the coupling: feats_direction_coupling_STiPDC.m
  • includes the 2D fractal measure: fd_curves.m

For more information on function type help <filename.m>.

Examples

Synthetic signals

To generate the 4 time-varying bi-variate autoregressive examples and plot (Figure 3):

>> gen_STiPDC_all_signals;

Example of the short-time information partial directed coherence (ST-iPDC) function (middle), with the time-vary coupling coefficients (top) and the spectral representations of the signals x and y:

2D fractal dimension

Plot examples for 2D fractal dimension measure an extension of the Higuchi approach (Figure 2):

>> plot_FD_examples;

Which plots the following fractal dimension estimates D for 3 different planar signals:

Estimating instantaneous frequency in the STiPDC

Estimate and plot the instantaneous frequency (IF) estimates from one of the synthetic signals (Figure 4A):

>> estimate_IF_STiPDC_example;

which will produce estimates for the 1,000 iterations:

To estimate features from the IF see help feats_IF_STiPDC.m.

Time-varying directional coupling

Plot the time-varying directional coupling between the bi-variate signals (2 examples in Figure 4B and 4C):

>> time_traj_coupling_examples;

which will produce the trajectory plots for 2 different signal types:

To estimate features from the IF see help feats_direction_coupling_STiPDC.m.

References

1. Baccala LA, Takahashi DY, & Sameshima K. (2016). Directed Transfer Function: Unified Asymptotic Theory and Some of its Implications. IEEE Transactions on Biomedical Engineering, 63(12), 2450–2460. doi:10.1109/TBME.2016.2550199

2. Baccala LA, de Brito CSN, Takahashi DY, & Sameshima K. (2013). Unified asymptotic theory for all partial directed coherence forms. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 371(1997), 20120158. doi:10.1098/rsta.2012.0158

Contact

John M. O' Toole

INFANT Research Centre, Ireland. Department of Paediatrics and Child Health,
Room 2.19, UCC Paediatric Academic Unit, Cork University Hospital,
University College Cork, Ireland.

Email: jotoole AT ucc. ie