Software Open Access
Transfer Entropy With Partitioning
Joon Lee , Shamim Nemati , Ikaro Silva
Published: March 4, 2016. Version: 1.0.0
New software package added to PhysioToolkit: TEWP (March 4, 2016, 1 a.m.)
The Transfer Entropy With Partitioning package is a repository of MATLAB functions that can estimate transfer entropy (information flow) from one time series to another using a non-parametric partitioning algorithm. Also included is an example data set that the implemented algorithms can be applied to.
Please include the standard citation for PhysioNet:
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Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.
Description
This is a repository of MATLAB functions that can estimate transfer entropy (information flow) from one time series to another using a non-parametric partitioning algorithm. Also included is an example data set that the implemented algorithms can be applied to. The functions were tested in MATLAB R2016b on 03 March 2016.
Usage
There are three functions used to calculate transfer entropy between time series using different methods of probability density estimation:
- [T] = transferEntropyKDE(X,Y,t,w,N,bw_coeff)
- Based on Guassian kernel density estimation.
- Calls function mdKDE
- [T nPar dimPar]=transferEntropyPartition(X,Y,t,w)
- Based on the Darbellay-Vajda partitioning algorithm
- Calls function DVpartition3D
- [T] = transferEntropyRank(X,Y,l,k,t,w,Q)
- Based on bin counting with fixed and equally-spaced bins.
- Calls function quantentr
The MAT file 'example_data.mat' contains a 1-D structure array of example data that harnesses an information flow from X to Y at a lag of 2 according to: y(i)=[(1+a)*x(i-2)]^2
Different values of the coupling constant 'a' were simulated, along with associated levels of Laplacian noise given by SNR levels.
- For a demonstration, run 'demoscript.m'. The plotted results should look like figure 1.
- See the individual .m function files for information on how to call them.
- See readme_example_data.txt for information on the example data parameters.
- See Measuring Information Transfer, Physical Review Letters, 85(2):461-464, 2000 for more details on the methods.
Access
Access Policy:
Anyone can access the files, as long as they conform to the terms of the specified license.
License (for files):
Open Data Commons Attribution License v1.0
Discovery
Topics:
entropy
complexity
Corresponding Author
Files
Total uncompressed size: 152.1 KB.
Access the files
-
Download the files using your terminal:
wget -r -N -c -np https://physionet.org/files/tewp/1.0.0/
-
Download the files using AWS command line tools:
aws s3 sync --no-sign-request s3://physionet-open/tewp/1.0.0/ DESTINATION
| Name | Size | Modified |
|---|---|---|
| COPYING (download) | 34.3 KB | 2019-04-12 |
| DVpartition3D.m (download) | 3.7 KB | 2019-04-12 |
| demoresults.jpg (download) | 18.1 KB | 2019-04-12 |
| demoscript.m (download) | 606 B | 2019-04-12 |
| example_data.mat (download) | 84.4 KB | 2019-04-12 |
| mdKDE.m (download) | 1.6 KB | 2019-04-12 |
| quantentr.m (download) | 1.3 KB | 2019-04-12 |
| readme_example_data.txt (download) | 703 B | 2019-04-12 |
| transferEntropyKDE.m (download) | 2.5 KB | 2019-04-12 |
| transferEntropyPartition.m (download) | 2.4 KB | 2019-04-12 |
| transferEntropyRank.m (download) | 2.4 KB | 2019-04-12 |
