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+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +5251 +10.21105/joss.05251 + +ReSurfEMG: A Python library for preprocessing and +analysis of respiratory EMG. + + + +https://orcid.org/0000-0003-1672-7565 + +Moore +Candace Makeda + + + + +https://orcid.org/0000-0001-8888-4792 + +Baccinelli +Walter + + + + + +Sivokon +Oleg + + + + +https://orcid.org/0000-0001-9443-4069 + +Warnaar +Robertus Simon Petrus + + + + +https://orcid.org/0000-0002-0150-306X + +Oppersma +Eline + + + + + +Netherlands eScience Center, Amsterdam, The +Netherlands + + + + +Bright Computing / NVIDIA, Amsterdam, The +Netherlands + + + + +Cardiovascular and Respiratory Physiology Group, Technical +Medical Centre, University of Twente, Enschede, the +Netherlands. + + + + +22 +1 +2023 + +8 +90 +5251 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +Python +respiratory surface EMG +respiratory EMG +signal processing + + + + + + Summary +

ReSurfEMG is an open-source collaborative Python library for + analysis of respiratory surface electromyography (EMG). Analysis and + interpretation of respiratory surface EMG requires expertise of both + clinicians and technical specialists including software engineers, for + example to identify the onset and offset of respiratory effort. This + necessary cooperation of clinicians and technical staff implies that + respiratory EMG data must be considered by groups of professionals who + differ profoundly in computational skill levels. Therefore, our + package implements different processing and analytic pipelines for + researchers to investigate, through several interfaces. The package + not only allows for pre-processing and analysis with either command + line or Jupyter notebooks, it supports a graphic user interface + package which allows code-free interaction with the data, the + ReSurfEMG-dashboard. + The interface for ReSurfEMG algorithms through Jupyter notebooks + allows researchers to run preset experiments, potentially extend them + and record the results in csv files automatically. ReSurfEMG code + allows for analysis of various parameters in respiratory surface EMG + from simple ones as peak amplitude to area under curve and the + potential for more complex ones such as entropy. These characteristics + can be used in machine learning models, for which there is also code + in the notebooks to demonstrate. The current state of research in the + field includes scientific work without any published code and + therefore it is currently difficult to compare outcome parameters in + (clinical) research. Filter settings as cutoff frequencies and window + lengths to create a respiratory tracing may differ, as well as the + used algorithms for feature extraction. This library enables + communication towards reproducible research.

+ + + State of the field +

Respiratory surface EMG signals can be processed, with a limited + group of algorithms, by several existing libraries which deal with EMG + signals in general. However, none of these libraries have been + extended publicly to cover all the specifics of the respiratory + signal. BioSPPy + (Carreiras + et al., 2015--), and NeuroKit + (Makowski + et al., 2021) both cover processing for various + electrophysiological signals, and have modules for general EMG + processing. Unfortunately, these modules do not have code specific for + respiratory surface EMG. pyemgpipline + (Wu et + al., 2022) is specific to EMG, but not to respiratory EMG. + Without functions specific to respiratory EMG, researchers must code + themselves the functions for extracting even basic parameters reported + in current literature, such as area under curve. This is beyond the + technical skills of many researchers in the field, and hinders the + quality and the reproducibility of the results. Therefore, ReSurfEMG + bridges the gaps in clinical researcher abilities, allowing + researchers at a low level of technical skill to analyze respiratory + EMG.

+ + + Statement of need +

When the diaphragm and/or other respiratory muscles fail, breathing + needs mechanical support. To prevent further failure of respiratory + muscles and optimize treatment, it is essential to monitor respiratory + muscle activity. Muscle activity can be measured via an electromyogram + (EMG) invasively or by surface electrodes attached to the skin. + Although surface electrodes have the advantage of non-invasiveness, + they bring challenges in data processing by patient characteristics + and crosstalk of other muscles. Yet, preprocessing and analysis of + these inherently complex EMG data sets remains very limited due to + various factors including proprietary software. At present there is a + lack of internationally accepted respiratory surface EMG processing + conventions. For example, in order to determine where in a signal a + breath, or inspiratory effort, begins one must determine the onset of + muscle activation. However, there are at least seven algorithms for + muscle effort onset detection in the existing literature + (Hodges + & Bui, 1996), + (Bonato + et al., 1998), + (Lidierth, + 1986), + (Abbink + et al., 1998), + (Solnik + et al., 2010), + (Silva + et al., 2013) + (Londral + et al., 2013), and researchers sometimes simply determine the + onset of muscle activity manually. Determining respiratory onset and + offset is even more complex than the activation of muscle effort as + there are inspiratory and expiratory muscles which may function + paradoxically in pathological states, as well as activity of the heart + adding noise to raw EMG signals. As there is no consensus on how to + define respiratory effort onset and offset, or on the appropriate + preprocessing algorithms to remove noise from the respiratory signal, + of which the cardiac activity is most prominent, comparing research + across groups is extremely difficult. This package aims to create open + preprocessing and analysis pipelines to advance the use of this signal + in clinical research that can be compared across institutions and + across various acquisition hardware set-ups.

+ + + Used by +

This work supports ongoing research in respiratory surface EMG by + the Cardiovascular and Respiratory Physiology group at the University + of Twente.

+ + + Acknowledgements +

This work is part of the research project Development of a software + tool for automated surface EMG analysis of respiratory muscles during + mechanical ventilation, which is supported by the Netherlands eScience + Center and the University of Twente.

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