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MINI REVIEW article

Front. Netw. Physiol.
Sec. Networks in the Cardiovascular System
Volume 4 - 2024 | doi: 10.3389/fnetp.2024.1478280
This article is part of the Research Topic Evaluated Methods for Signal Analysis: Promoting Open Science in Network Physiology View all articles

Physiological Signal Analysis and Open science based on the Julia language and associated software

Provisionally accepted
George Datseris George Datseris 1*Jacob S. Zelko Jacob S. Zelko 2
  • 1 University of Exeter, Exeter, England, United Kingdom
  • 2 The Roux Institute, Northeastern University, Portland, United States

The final, formatted version of the article will be published soon.

    In this mini-review, we propose the usage of the Julia programming language and its software as a strong candidate for reproducible, efficient, and sustainable physiological signal analysis. First, we highlight available software and Julia communities, that already provide top-of-the-class algorithms for all aspects of physiological signal processing, despite the language's relatively young age. Julia can significantly accelerate both research and software development due to it being a high level interactive language with high performance code generation. It is also particularly suited for open and reproducible science. Openness is supported and welcomed because the overwhelming majority of Julia software are open source and developed openly on public platforms primarily by individual contributions. Such an environment makes it more likely that an individual not (originally) associated with a software, would still be willing to put their code there, further promoting code sharing and re-use. On the other hand, Julia's exceptionally strong package manager and surrounding ecosystem allows easily making self-contained, reproducible projects that can be instantly installed and run irrespective of processor architecture or operating system.

    Keywords: digital signal processing, physiological signals, complexity measures, Julia, timeseries analysis, Reproducible, Open Science

    Received: 09 Aug 2024; Accepted: 08 Oct 2024.

    Copyright: © 2024 Datseris and Zelko. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: George Datseris, University of Exeter, Exeter, EX4 4PY, England, United Kingdom

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.