Software Open Access

# Transfer Entropy With Partitioning

Published: March 4, 2016. Version: 1.0.0

When using this resource, please cite the original publication:

Lee et al. Transfer Entropy Estimation and Directional Coupling Change Detection in Biomedical Time Series. Biomedical Engineering Online 2012 11:19.

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 have been 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.

### Acknowledgments

This package was developed by J. Lee, S. Nemati, and I. Silvia.

##### Access

Access Policy:
Anyone can access the files, as long as they conform to the terms of the specified license.

##### Corresponding Author
You must be logged in to view the contact information.

## Files

Total uncompressed size: 152.1 KB.

##### Access the files
wget -r -N -c -np https://physionet.org/files/tewp/1.0.0/
{"ft_lang_label":"__label__en","ft_lang_prob":0.6519759,"math_prob":0.6072585,"size":3156,"snap":"2020-34-2020-40","text_gpt3_token_len":869,"char_repetition_ratio":0.14467005,"word_repetition_ratio":0.0,"special_character_ratio":0.28707224,"punctuation_ratio":0.19329074,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.962472,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2020-09-24T08:08:08Z\",\"WARC-Record-ID\":\"<urn:uuid:8412f8ff-bd39-4fe2-8f31-867b9eca52df>\",\"Content-Length\":\"19481\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:b180d9d3-9539-4207-9b7e-3e5de3ea6460>\",\"WARC-Concurrent-To\":\"<urn:uuid:e63dcbd7-b023-43e3-80b6-bc90529b2886>\",\"WARC-IP-Address\":\"18.13.52.205\",\"WARC-Target-URI\":\"https://www.physionet.org/content/tewp/1.0.0/\",\"WARC-Payload-Digest\":\"sha1:7W5ZRC7OCFPIGXVNC4HSMRZWJWUYLPWE\",\"WARC-Block-Digest\":\"sha1:O6W2KQPO6KLUZ7WCURLCK2BTD2LRRYNT\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2020/CC-MAIN-2020-40/CC-MAIN-2020-40_segments_1600400214347.17_warc_CC-MAIN-20200924065412-20200924095412-00398.warc.gz\"}"}