RapidHRV is a data processing pipeline for the analysis and visualization of cardiac data.
Please provide credit where appropriate:
Kirk, P. A., Bryan, A. D., Garfinkel, S. N., & Robinson, O. J. (2022). RapidHRV: An open-source toolbox for extracting heart rate and heart rate variability. PeerJ, 10, e13147. https://doi.org/10.7717/peerj.13147
This library is distributed under an MIT License
pip install rapidhrvGiven a numpy array, or something convertable to it (such as a list),
rapidhrv.preprocess can generate input suitable for analysis with
rapidhrv.analyze, which will return a pandas dataframe containing HRV data.
import numpy as np
import rapidhrv as rhv
my_data = np.load("my_data.npy") # Load data
data = rhv.Signal(my_data, sample_rate=50) # Convert to rhv Signal class
preprocessed = rhv.preprocess(data) # Preprocess: may interpolate data, check the docstring on `rapidhrv.preprocess`
result = rhv.analyze(preprocessed) # Analyze signalPlease see the included tutorial notebook.
In order to get a working development environment,
please install Poetry for your platform,
and run poetry install to generate a virtual environment.
If you plan on making any changes to the included notebooks,
please run nbstripout --install from within the poetry venv before committing any changes.
To run said notebooks from the environment provided by poetry,
install the required dependencies with poetry install --extras notebooks.