About this Research Topic
The field of network physiology offers a plethora of open-source algorithms and data sets for multimodal biosignal (ECG, EEG, etc.) analysis. Typical examples are algorithms for the characterization of physiological states or methods for detecting coupling directions and strengths in networks of interacting units. However, as the field of software development is so fast-paced, working environments are changing rapidly, rendering source code or binary tools quickly outdated. Furthermore, due to lack of standardization, in many cases relevant parameters are not documented or important details of the algorithms are missing in scientific publications. These aspects and the increasing availability of many tools also increase the risk of misuse and a loss of reproducibility, endangering good scientific practice. To counteract these problems we propose to carefully evaluate existing and new algorithms including systematic testing and careful documentation. In this sense the proposed research topic is aiming at stimulating and coordinating a collective attempt to achieve this goal.
The goal of this research topic is to:
i) Promote open science in network physiology by method evaluation and consolidation,
ii) Guarantee and maintain quality of already existing and new open-source algorithms that have to be well documented, tested and benchmarked using freely-available data sets.
Furthermore, stability and reproducibility have to be achieved to cope with rapidly changing software development and working environments. This research topic aims for providing a platform to achieve these goals in a joint effort where many experts carefully analyze and scrutinize biosignal analysis methods and their potential fields of application. By doing so, the pros and cons of different methods shall be uncovered and documented for future use in research.
The main goal of this Research Topic is to present a list of well tested algorithms for biosignal analysis in Network Physiology, including implementation in one or several common programming languages and detailed documentation. All methods, already existing or new, should be evaluated and compared using freely-available data sets.
Methods may cover the whole processing chain of signal processing, including but are not limited to: signal classification, detection of information flow and coupling directions, dimension reduction and visualization, (automatic) clustering, detection and prediction of extreme events, cross prediction and forecasting of physiological variables, data-driven modelling, preprocessing (missing data points, noise removal, ...), and multimodal approaches combining multiple biosignals.
Next to the submitted manuscript, we expect that each paper contains a link to a freely-available version control system (e.g. Github, Gitlab) offering the source and a README to reproduce the results reported in the paper.
Topic Editor Dr. Alexander Schlemmer is Co-Founder and External Scientific Advisor for IndiScale GmbH, Germany. All other Topic Editors declare no competing interests with regards to the Research Topic subject
The goal of this research topic is to:
i) Promote open science in network physiology by method evaluation and consolidation,
ii) Guarantee and maintain quality of already existing and new open-source algorithms that have to be well documented, tested and benchmarked using freely-available data sets.
Furthermore, stability and reproducibility have to be achieved to cope with rapidly changing software development and working environments. This research topic aims for providing a platform to achieve these goals in a joint effort where many experts carefully analyze and scrutinize biosignal analysis methods and their potential fields of application. By doing so, the pros and cons of different methods shall be uncovered and documented for future use in research.
The main goal of this Research Topic is to present a list of well tested algorithms for biosignal analysis in Network Physiology, including implementation in one or several common programming languages and detailed documentation. All methods, already existing or new, should be evaluated and compared using freely-available data sets.
Methods may cover the whole processing chain of signal processing, including but are not limited to: signal classification, detection of information flow and coupling directions, dimension reduction and visualization, (automatic) clustering, detection and prediction of extreme events, cross prediction and forecasting of physiological variables, data-driven modelling, preprocessing (missing data points, noise removal, ...), and multimodal approaches combining multiple biosignals.
Next to the submitted manuscript, we expect that each paper contains a link to a freely-available version control system (e.g. Github, Gitlab) offering the source and a README to reproduce the results reported in the paper.
Topic Editor Dr. Alexander Schlemmer is Co-Founder and External Scientific Advisor for IndiScale GmbH, Germany. All other Topic Editors declare no competing interests with regards to the Research Topic subject
Keywords: Reproducible research, benchmarking, evaluation of methods, good scientific practice, time series analysis, open source, network physiology
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
