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Anne Humeau-Heurtier received the PhD degree in Biomedical Engineering in France. She is currently a full professor in Engineering with the University of Angers, France. Her research interests include signal and image processing, mainly multiscale and entropy-based analyses, and data-driven methods. Her main applications are related to the biomedical field. She is associate editor for IEEE Transactions on Biomedical Circuits and Systems, for Frontiers in Network Physiology - Information Theory, Causality & Control, and for the Engineering Medicine and Biological Society Conference. She is member of the editorial board for the journal Entropy and area editor on Signal Processing for the IEEE Open Journal of Engineering in Medicine and Biology. She is also member of the IEEE-EMBS Technical Community on Cardiopulmonary Systems and Physiology-based Engineering. She has been guest editor for special issues in journals as Entropy, Complexity, and Computational and Mathematical Methods in Medicine.

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Luca Faes is Professor of Biomedical Engineering at the University of Palermo, Italy, where he teaches courses on Statistical Analysis of Biomedical Signals, Biosensors and Biomedical Devices. He has previously been with the Department of Physics of the University of Trento, Italy, and visiting scientist at the State University of New York, Worcester Polytechnic Institute, University of Gent, University of Minas Gerais, and Boston University. Dr. Faes is Senior Member of the IEEE, member of the IEEE Engineering in Medicine and Biology Society (IEEE-EMBS) and of the Technical Committee of Biomedical Signal Processing, and member of the European Study Group on Cardiovascular Oscillations (ESGCO). He is Specialty Chief Editor for Frontiers in Network Physiology, and Associate Editor for IEEE Transactions on Biomedical Engineering and Entropy. His research focuses on multivariate time series analysis and information theory applied to cardiovascular neuroscience, brain connectivity, brain-heart interactions, and network physiology. Within these fields, he co-authored eight book chapters and more than ~250 peer-reviewed publications, receiving more than 6000 citations (h-index: 46; font: Scholar).

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Hamed Azami received the Ph.D. degree in biomedical signal processing from the Institute for Digital Communication, University of Edinburgh, U.K., in 2018. He is currently a Research Fellow in biomedical signal processing and machine learning at The Centre for Addiction and Mental Health, Toronto University, Toronto, ON, CA. Before that, he held a Postdoctoral position at Massachusetts General Hospital and Harvard University, Boston, MA, USA until 2020. His main research interests include biomedical signal processing, nonlinear analysis, and machine learning. He has been working with a range of entropy metrics for data analysis, including permutation entropy, fuzzy entropy, and their multiscale, multivariate, and multi-dimensional formulations. Most notably, though, he proposed the very influential concept of Dispersion Entropy. He obtained an award for the best paper published in Medical & Biological Engineering & Computing in 2017 (selected among >170 articles). He has published two book chapters and over 40 articles in peer-reviewed journals, receiving over 2,300 citations (h-index is 24).

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Dr Javier Escudero is senior lecturer in biomedical signal processing at the University of Edinburgh, UK. After completing his PhD in signal processing in 2010 at the University of Valladolid, Spain, he obtained a postdoctoral position at the University of Plymouth, UK, in a project funded by the National Institute for Health Research where he developed and applied machine learning algorithms for alternative end point measures in clinical trials. An article resulting from this work was awarded the prize for top paper published in British Journal of General Practice – Open. In 2013, Dr Escudero joined the University of Edinburgh with a tenure-track position. Since then, he has created and consolidated an independent research group in biomedical signal processing within the School of Engineering. He is an international leader in the development of nonlinear analysis techniques and their application to characterise biomedical signals, contributing nonlinear algorithms that are fast to compute and robust to noise. A significant proportion of this work focuses on characterising brain activity. This includes one article awarded the Nightingale Award to the best paper published in Medical & Biological Engineering & Computing in 2017 (selected among >170 articles). His research in this area has also been recognised by invitations to international workshops supported by the International Federation of Clinical Neurophysiology, and other international bodies. Dr Escudero has received funding from, among others, the Leverhulme Trust, the UK’s Engineering and Physical Sciences Research Council and Medical Research Council, and the Chief Scientist Office of Scotland. He is currently leading the development of entropy analysis techniques for graph signals. He has published over 90 articles in peer-reviewed journals, receiving over 5,000 citations (h-index is 38, https://www.research.ed.ac.uk/en/persons/javier-escudero-rodriguez).