RecruitmentCurveFitting 1.1.2

A package for fitting sigmoid and bell-shaped functions to EMG recruitment-curve measurements

This package provides classes Sigmoid and HillCurve for fitting, plotting and interpolating EMG recruitment curves. These might be suitable for the M-wave and H-reflex components, respectively, of the EMG responses to electrical peripheral nerve stimulation. Feed in a sequence of stimulation intensities and corresponding sequence of response magnitudes. These can be used from the command-line, outside of python.

The package also provides a higher-level class RecruitmentCurve which takes in EMG waveforms, together with information on the start and end times of the response components of interest, and performs either or both of the above fits, optionally rendering the results on an interactive plot.

To get started:

python -m pip install RecruitmentCurveFitting
python -m RecruitmentCurveFitting --unpack-examples
python -m RecruitmentCurveFitting example-data1.txt example-data2.txt --plot
python -m RecruitmentCurveFitting --help
python -m RecruitmentCurveFitting --help-module

If you use this software in your research, your report should cite the article in which this approach was introduced, as follows:

• McKinnon ML, Hill NJ, Carp JS, Dellenbach B & Thompson AK (2023). Methods for automated delineation and assessment of EMG responses evoked by peripheral nerve stimulation in diagnostic and closed-loop therapeutic applications. Journal of Neural Engineering (in press) https://doi.org/10.1088/1741-2552/ace6fb

The corresponding BibTeX entry is:

@article{mckinnonhill2023,
  author  = {McKinnon, Michael L. and Hill, N. Jeremy and Carp, Jonathan S.
             and Dellenbach, Blair and Thompson, Aiko K.},
  title   = {Methods for Automated Delineation and Assessment of {EMG}
             Responses Evoked by Peripheral Nerve Stimulation in Diagnostic
             and Closed-Loop Therapeutic Applications},
  journal = {Journal of Neural Engineering},
  year    = {2023},
  doi     = {10.1088/1741-2552/ace6fb},
  url     = {https://doi.org/10.1088/1741-2552/ace6fb},
}