Bubble Entropy
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 mdimensional space. We use the bubble sort
algorithm to sort each vector in the mdimensional space and count the number
of swaps performed for each vector. Doing so, we create a more coarsegrained
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
Selected Publications:

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]