Date: 6 March 2024
Time: 10:00 AM ET (New York Time)
Presenter(s): Javier Escudero
Original article: Download Open Access article
In the field of time series analysis, the application of entropy metrics, particularly permutation entropy, is pivotal for quantifying system state probabilities and the complexity of signals. While these metrics have been extensively applied to uni-variate time series, and recently extended to two-dimensional data like images, their application to irregular domains such as graphs has remained unexplored.
Here, the authors present a ground-breaking shift in this field by generalizing the concept of permutation entropy for the analysis of signals on irregular graphs. The authors have expanded the scope of permutation entropy, employing a method that compares signal values across neighboring nodes using the graph's adjacency matrix. This generalization is not merely an adaptation, but a significant enhancement of the classical permutation entropy applied to time series, extending its applicability. The authors approach integrates signal values with the graph topology and provides a unifying framework for the analysis of uni- and multi-variate time series, images, and graph signals.
This advancement has shown promising initial results including industrial applications and notably MRI brain signal analysis in the context of early Alzheimer’s disease, offering a new perspective and tools for analyzing complex systems data.
Javier Escudero (S’07-M’10-SM’19) received the M.Eng. and Ph.D. degrees in telecommunications engineering from the University of Valladolid (Spain) in 2005 and 2010, respectively.
He held a postdoctoral position at the University of Plymouth (UK) From 2010 to 2013. He then moved to the School of Engineering at the University of Edinburgh (UK), where he created and leads a multidisciplinary research group. He is currently a Reader (equivalent to associate profession) in biomedical signal processing. He has published over 90 journal articles and over 60 peer-reviewed conference papers. His research interests include nonlinear analysis, network analysis and graph signals, and data science applications to healthcare.
Dr. Escudero is member of the Complex Systems Society and of the IEEE Signal Processing Society Bio Imaging and Signal Processing Technical Committee and of the IEEE Engineering in Medicine and Biology Society Biomedical Signal Processing Technical Committee.