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Recent Advances in Nonlinear Biomedical Systems Analysis
Submission DeadlineMay 30, 2020

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Lead Guest Editor
Signals and Images Processing Center, Universidad Tecnológica Nacional, Buenos Aires, Argentina
Guest Editors
  • Ricardo Luis Armentano
    Bio Engineering Research and Development Group, Facultad Regional Buenos Aires, Universidad Tecnológica Nacional, Buenos Aires, Argentina
  • Eugenio Otal
    Department of Chemistry and Materials, Faculty of Textile Science and Technology, Shinshu University, Nagano, Japan
  • Manuela Kim
    Department of Chemistry and Materials, Faculty of Textile Science and Technology, Shinshu University, Nagano, Japan
  • Leandro Cymberknop
    Bio Engineering Research and Development Group, Faculty Regional Buenos Aires, Universidad Tecnológica Nacional, Buenos Aires, Argentina
  • Gustavo Gil
    Department of Robotics, Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier, Montpellier, France
Introduction
There are many ways to study a signal or data series arising from a bio-system, and many of them contributes to elaborate a wide view of the object or process under analysis, particularly when the number of variables or the volume of data is large. Added to the commented above in the research of the biomedical systems is very frequent to find a non-linear behaviour. This kind of behaviour demands for its study the continuous development of scientific tools to be capable of make a deep analysis of the information contained in it.
The field of non-linear signal and system analysis had a growing interest in the past forty years.
In particular, during the time elapsed in the twenty-one century the development of new mathematical concepts and innovative computational techniques contributes to improve the knowledge in many subjects of the biomedical and bioengineering research such as informational theory tools, data mining, neural networks, some statistical physics concepts, multivariate statistics, deep learning, among others.
The discrimination of state of health and disease, fractal cardiac rate variability, the use of entropic measures in epilepsy, characterization of chaotic cardiac behaviour and the study of biomedical signal under several scales are a small sample of the giant set of examples that anyone can found in the scientific literature on the application of newest techniques in biomedicine.
In this manner this new findings impact not only in the academic or scientific research otherwise in practically all subjects of the medical knowledge including in some cases a feedback process over the acquisition devices or acquisition techniques.
The application of non-linear signal and data analysis in biosystems has been used from a while, up to now however the massive diffusion didn’t come arrive, in general this tool is kept in a moderated size academic circle.
The strategic pathway to increase the application of non-linear concepts in biomedical systems should be complemented with a intensive human resources training, that requires literature and bibliographic material to enrich to the young researchers.
The power and capability of the tools coming from non-linear system analysis as well as provided by multivariate statistics has been successfully probed.
Since the preventive medicine or the medical science in general is assisted by remarkable developments in the field of the technology associated with them, the volume of information as well the complexity of the data that generates is in continuously growing. Actually a really big set of biomedical data is collected in any centre of medical assistance around the world. The whole progress in those fields of the technology and medical sciences demands day by day more reliable diagnostic techniques, and for the kind of complexity owing to the systems itself a deep understand of the process concerned in all stages of the medical work.
In consequence, all the efforts doing to extend the use of the above-mentioned concepts in the field of biomedical knowledge will contribute without any kind of doubts in benefits of the society.
Aims and Scope:
  1. Chaotic Systems Analysis
  2. Informational Measures
  3. Entropy
  4. Multivariate Statistics
  5. Complexity
  6. Deep Learning
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