An Entropy almost free of Parameters
A critical point in any definition of entropy is the selection of the parameters employed to obtain an estimate in practice. We propose a new definition of entropy aiming to reduce the significance of this selection. We call the new definition Bubble Entropy.
In this definition, we embed the time series in the m-dimensional space. We use the bubble sort algorithm to sort each vector in the m-dimensional space and count the number of swaps performed for each vector. Doing so, we create a more coarse-grained distribution and then compute the entropy of this distribution.
The definition proposed is almost free of parameters. The most common ones are the scale factor r and the embedding dimension m. In our definition, the scale factor is totally eliminated and the importance of m is significantly reduced. The proposed method presents increased stability and discriminating power.
Contact: manis -at- cs -dot- uoi -dot- gr
- George Manis, Md Aktaruzzaman and Roberto Sassi, “Bubble entropy: an entropy almost free of parameters,” IEEE Transactions on Biomedical Engineering, vol. 64, no. 11, pp. 1558–2531, Nov. 2017 [link]
- George Manis and Roberto Sassi, “Tolerance to spikes: a comparison of sample and bubble entropy,’’ in Proc. of Computing in Cardiology, Rennes, France, 2017 [link]