Interpretation of the Lempel-Ziv Complexity Measure in the Context of Biomedical Signal Analysis
By M. Aboy, Roberto Hornero, Daniel E. Abásolo, D. Álvarez. 2006
402 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Molecular Biology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Bernhard Schölkopf
- Dominik Janzing
- Jan Peters
- Bastian Steudel
- Kun Zhang
- David Lopez-Paz
- Joris M. Mooij
- Arthur Gretton
In Computational Theory and Mathematics
- Bernhard Schölkopf
- Dominik Janzing
- Bastian Steudel
- Kun Zhang
- Hamed Azami
- Mostafa Rostaghi
- Jan Peters
- Jilles Vreeken
In Molecular Biology
- Bernhard Schölkopf
- Dominik Janzing
- Bastian Steudel
- Hamed Azami
- Mostafa Rostaghi
- Michel Besserve
- Arthur Gretton
- Rémy Sun
Citations in the corpus
- Causal Markov condition for submodular information measures (2010, background): “To this end, instead of defining complexity with respect to a universal Turing machine we explicitly limit ourselves to specific production processes of strings.”
- Quantifying Neural Correlations Using Lempel-Ziv Complexity (2008, neutral): “Several studies of neural spike trains have already used Lempel-Ziv complexity [4, 5, 6].”
- Dispersion Entropy: A Measure for Time-Series Analysis (2016, methodology): “…a signal including five segments of colored noise with increasing noise bandwidth, a quasi-periodic signal with increasing additive noise power, an autoregressive (AR) process, and a MIX process evolving from randomness to periodic oscillations are used to evaluate the proposed method [7], [8].”