ReSurfEMG is an open source collaborative python library for analysis of respiratory electromyography (EMG). On the same site as the repository for this library we keep related resources.

ReSurfEMG includes a main code library where the user can access the code to change various filter and analysis settings directly and/or in our researcher interface notebooks. In addition, ReSurfEMG has a dashboard interface which contains default settings for preprocessing and analysis which can be changed through a graphical (no code) interface. We have some functionality available through a command line interface as well.

The library was initially built for surface EMG, however many functions will also work for invasively measured respiratory EMG. This library supports the ongoing research at University of Twente on respiratory EMG.

The core functions of ReSurfEMG are in the folder resurfemg:

• cli: Scripts for the command line interface
• config: Configure all paths for data analysis
• data_connector: Converter functions for various hardware/software and the TMSisdk lite module
• helper_functions: General functions to support the functions in this repository
• machine_learning: Run machine learning algorithms on arrays
• post-processing: Calculate features from the respiratory data:
  • entropy
  • area under curve
  • time under curve
  • slope
  • peak in breath
  • variability
  • SampEn adapted from nolds package
• pre_processing: Process the raw EMG signal
  • ecg-removal: ICA and gating
  • envelope: RMS and smoothers
  • filtering: cutters, low-, high- and bandpass, notchfilter, computer power loss
• visualization: Show powerspectrum

Our guide to notebooks is under construction. To look around keep in mind the following distinction on folders:

researcher_interface:

• These are a growing series of interactive notebooks that allow researchers to investigate questions about their own EMG data
  • ⚡ Important: in almost all data there will be a time difference between EMG signals and ventilator signals. You can pre-process to correct for this lead or lag with the notebook called lead_lag_mismatch_upsample.

open_work:

• This folder contains experimental work by core members of the ReSurfEMG team (Dr. Eline Mos-Oppersma, Rob Warnaar, Dr. Walter Baccinelli and Dr. Candace Makeda Moore)

The notebooks are configured to run on various datasets. Contact Dr. Eline Mos-Oppersma( 📫 e.mos-oppersma@utwente.nl) to discuss any questions on data configuration for your datasets.

If you want to use a standardized dataset for any purpose we recommend the data in the ReSurfEMG/synthetic_data repository

Data there can be used with any respiratory EMG algorithms in any program. Thus that data can function as a benchmarking set to compare algorithms across different programs.

In order to preprocess and/or to train models the code needs to be able to locate the raw data you want it to find.

There are several ways to specify the location of the following directories:

• root_emg_directory: Special directory. The rest of the directory layout can be derived from its location.
• preprocessed: The directory that will be used by preprocessing code to output to.
• models: The directory to output trained models to.

You can store this information persistently in several locations.

1. In the same directory where you run the script (or the notebook). e.g. ./config.json.
2. In home directory, e.g. ~/.resurfemg/config.json.
3. In global directory, e.g. /etc/resurfemg/config.json.

However, we highly recommend you use the home directory. This file can have this or similar contents:

{
    "root_emg_directory": "/mnt/data",
    "preprocessed": "/mnt/data/preprocessed",
    "models": "/mnt/data/models",
    "output": "/mnt/data/output",
}

The file is read as follows: if the files specifies root_emg_directory directory, then the missing entries are assumed to be relative to the root. You don't need to specify all entries.

You can get test data by extracting it from the Docker image like this:

mkdir -p not_pushed
cd ./not_pushed
docker create --name test-data crabbone/resurfemg-poly5-test-files:latest
docker cp test-data:/ReSurfEMG/tests/not_pushed/. .
docker rm -f test-data

ReSurfEMG is a pure Python package. Below is the list of platforms that should work. Other platforms may work, but have had less extensive testing. Please note that where python.org Python stated as supported, it means that versions 3.9 and 3.10 are supported.

	Linux	Win	OSX
	Supported	Supported	Unknown
	Supported	Supported	Supported

Installation with Anaconda/conda and/or mamba are the preffered methods. They are covered in the "Getting Started" section. If you wish to install with pip:

1. Create and activate a virtual environment (see developer setup section for more details)
2. Install ResurfEMG package by running pip install resurfemg.

How to get the notebooks running? Assuming the raw data set and metadata is available. Note for non-conda installations see next sections.

0. Assuming you are using conda for package management:

• Make sure you are in no environment:

  conda deactivate

  (optional repeat if you are in the base environment)

  You can build on your base environment if you want, or if you want to not use option A, you can go below it (no environment)

1. Option A: Fastest option: In a base-like environment with mamba installed, you can install all Python packages required, using mamba and the environment.yml file.

If you do not have mamba installed you can follow instructions here

• The command for Windows/Anaconda/Mamba users can be something like:

  mamba env create -f environment.yml

Option B: To work with the most current versions with the possibility for development: Install all Python packages required, using conda and the environment.yml file.

• The command for Windows/Anaconda users can be something like:

  conda env create -f environment.yml

• Linux users can create their own environment by hand (use .[dev] as in setup).

Make sure to enter your newly created environment.

Option C: In theory if you want to work, but never develop (i.e. add code), as a conda user with a stable (released) version create an empty environment, and install there:

• Create a blank environment with python pinned to 3.8 (assuming version < 0.1.0):

  conda create -n blank python=3.8

• Install within the blank environment

    conda activate blank
    conda install -c conda-forge -c resurfemg resurfemg jupyter ipympl

2. Open a notebook (we use Jupyter notebooks) in researcher_interface folder and interactively run the cells. You can use the command jupyter notebook to open a browser window on the folders of notebooks. Note, if you run with an installed library import appropriately. The basic_emg_analysis notebook can be used to understand how to use the package.

We distinguish between people who want to use this library in their own code and/or analysis and people who also want to develop this library who we call developers, be it as members of our team or independent contributors. People who simply want to use our library need to install the packaged version from one of the package indexes to which we publish released versions (eg. PyPI). This section of the readme is for advanced developers who want to modify the library code (and possibly contribute their changes back or eventually publish thier own modified fork). NB: you can accomplish modifications of the code, submit PRs and soforth without a 'developer's setup' but we feel this setup will make advanced contributions easier.

We have transitioned to a fully Python 3.9+ environment. (For older instructions with venv please see versions below 0.2.0, and adapt them if using Windows and/or a different Python version than Python.org Python. e.g. you may need to use .venv\Scripts\activate.bat in place of .venv/bin/activate) The instructions below are for our newer versions above 3.0.0. This will create a distributable package from the source code, then install it in the currently active environment. This will also install development tools we use s.a. pytest and codestyle and will also install tools we use for working with the library, s.a. jupyter:

After checking out the source code, create virtual environment. Both conda and venv environments are supported, however, if you are on Windows, we reccomend using conda.

1. Using Anaconda Python

   conda create -n resurfemg python=3.9
   conda activate resurfemg
   pip install -e .[dev]

2. Using PyPI Python

python3 -m venv .venv
source .venv/bin/activate

python -m venv .venv
.venv\Scripts\activate.bat
pip install -e .[dev]

Now you should have everything necessary to start working on the source code. Whenever you make any changes, re-run pip install -e .[dev] to see them applied in your environment.

Online documentation can be found at https://resurfemg.github.io/ReSurfEMG/ or on https://readthedocs.org/ by searching for ReSurfEMG. Up to date documentation can be generated in command-line as follows (in bash terminal):

python3 -m venv .venv
source .venv/bin/activate
pip install -e .[docs]
python setup.py apidoc
python setup.py build_sphinx

If you are working in a VScode command line interface (terminal cmd) should be more or less something like the following:

This is given with cmd.exe in mind:

python3 -m venv .venv
.venv\Scripts\activate.bat
pip install -e .[docs]
python setup.py apidoc
python setup.py build_sphinx

The project comes with several modifications to the typical default setup.py.

At the moment, the support for Anaconda Python is lacking. The instructions and the commands listed below will work in Anaconda installation but due to the difference in management of installed packages, the project will be installed into base environment regardless of the currently active one.

The project has a sub-project of a related dashboard. Dashboard is a GUI that exposes some of the project's functionality. In the past we kept a a legacy dashboard in the same repository with ReSurfEMG code but we have deleted it. The current version of the dashboard into it's own repository: https://github.com/ReSurfEMG/ReSurfEMG-dashboard

Please note that documentation is built using sphinx command for setuptools: setup.py build_sphinx, but sphinx is not installed as part of development dependencies, rather it is declared as a dependency of setup.py itself. There are cases when setup.py will not install its own dependencies. You are advised to install them manually.

• setup.py isort checks that the imports are properly formatted and sorted.
• setup.py apidoc generates RST outlines necessary to generate documentation.

The project doesn't include testing data. It was packaged into a Docker image which can be found at crabbone/resurfemg-poly5-test-files:latest. This test data was created by taking a signal from equipment, not a human, and certainly not a patient.

It is possible to run tests in container created from this image. Alternatively, you may download the image and extract directory /ReSurfEMG/tests/not_pushed into not_pushed in the root of the project and run:

python3 -m venv .venv
source .venv/bin/activate
pip install -e .[tests]
pytest

Below is a snippet that may help you to run the tests in a container:

docker run --rm -v $(pwd):/ci \
    --cap-add=SYS_ADMIN \
    --privileged=true \
    crabbone/resurfemg-poly5-test-files \
    sh -c 'set -xe
        cd /ci
        mkdir -p ./not_pushed/
        mount --bind /ReSurfEMG/tests/not_pushed/ ./not_pushed/
        pytest'

You will be able to preprocess, train and use models using command-line interface. You can also, in some cases, create files in the correct format for our Dashboard in a per folder batch process.

Below is an example of how to do that:

This will pre-process (with the alternative_a_pipeline_multi algorithm) the Poly5 files in the /mnt/data/originals directory, and output leads 1 and 2 preprocessed. (Note the \ symbol is simply a line-break and not meant to be included.)

python -m resurfemg acquire \
       --input /mnt/data/originals \
       --lead 1 --lead 2 \
       --output /mnt/data/preprocessed \
       --preprocessing alternative_a_pipeline_multi \

The following will run an ML model over all files:

python -m resurfemg ml |
        --input /mnt/data/preprocessed \
        --output /mnt/data/ml_output \
        --model  ml_models/finalized_lr_model_in_111.sav \

You can also make synthetic data. To explore this start with python -m resurfemg synth --help You can also make from horizontally formated csv files that can be read by the dashboard. To explore this start with python -m resurfemg save_np --help The help command is also available for ml and acquire.

All long options have short aliases.

✨Copyright 2022 Netherlands eScience Center and U. Twente Licensed under the Apache License, version 2.0. See LICENSE for details.✨