He received the PhD degree in engineering science majoring in signal, image and speech, from the Polytechnic National Institute of Grenoble, Grenoble, France, in 1998. He joined in 1999 the University of Orléans, France as Assistant Professor at the Polytech Orléans Engineering School and the Multidisciplinary Research laboratory in Systems Engineering, Mechanics and Energetics (PRISME). Currently, he works as a Full Professor in the IUT Chartres, University Institute of Technology, University of Orléans. He is in charge of the signal processing team in the PRISME laboratory. His main research interests include nonstationary time-frequency, time-scale and cyclo-stationary signal processing as well as information theory tools with applications to electrophysiological, electrical device and speech signals. He (co)authored 1 patent, 56 papers in peer-reviewed journals, 3 book chapters and more than 80 conference contributions. He is a member of IEEE; INTICC and GDR-ISIS societies. He has been guest editor for special issue in journals as journal Entropy.

Permutation entropy methods with applications

Among the entropy-based measures, the Permutation entropy (PE) and multiscale permutation entropy (MPE) are extensively used to measure irregularity in the analysis of time series, particularly in the context of biomedical signals. The spirit of these entropies relies on samples ranking used for building ordinal pattern distributions from which entropy measures are derived. As accuracy is crucial for researchers to obtain relevant interpretations, we will present some statistical results for PE and variants of PE. The tutorial will also focus on pitfalls and recommendations when interpreting the results of these measures applied on biomedical signals.

A practical session will illustrate both presentations by processing signals with various entropy measures. On the one hand, the participants will be invited to evaluate entropies on some typical simulated signals. On the other hand, they will have the opportunity to evaluate entropies on real data as potential indicators for assessing some sEMG characteristics. Several laptops with Matlab installed and complexity measures software will be available for data processing allowing a work in small groups.