https://github.com/Aura-healthcare/hrv-analysis
Package for Heart Rate Variability analysis in Python
https://github.com/Aura-healthcare/hrv-analysis
feature-engineering heart-rate-variability python rr-interval
Last synced: 17 days ago
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Package for Heart Rate Variability analysis in Python
- Host: GitHub
- URL: https://github.com/Aura-healthcare/hrv-analysis
- Owner: Aura-healthcare
- License: gpl-3.0
- Created: 2018-09-13T09:27:19.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2024-11-11T14:43:05.000Z (6 months ago)
- Last Synced: 2025-03-21T07:09:19.280Z (about 1 month ago)
- Topics: feature-engineering, heart-rate-variability, python, rr-interval
- Language: Python
- Homepage:
- Size: 8.97 MB
- Stars: 407
- Watchers: 9
- Forks: 98
- Open Issues: 21
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: code-of-conduct.md
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README
# Heart Rate Variability analysis
[](https://badge.fury.io/py/hrv-analysis)
[](https://github.com/Aura-healthcare/hrv-analysis/actions/workflows/ci.yml)
[](https://codecov.io/gh/Aura-healthcare/hrv-analysis)
[](https://www.gnu.org/licenses/gpl-3.0)
[](https://pepy.tech/project/hrv-analysis)
**hrvanalysis** is a Python module for Heart Rate Variability analysis of RR-intervals built on top of SciPy, AstroPy, Nolds and NumPy and distributed under the GPLv3 license.
The development of this library started in July 2018 as part of [Aura Healthcare](https://www.aura.healthcare) project, in [OCTO Technology](https://www.octo.com/fr) R&D team and is maintained by Robin Champseix.
**Full documentation** : https://aura-healthcare.github.io/hrv-analysis
**Website** : https://www.aura.healthcare
**Github** : https://github.com/Aura-healthcare
**Version** : 1.0.4
## Installation / Prerequisites
#### User installation
The easiest way to install hrv-analysis is using ``pip`` :
$ pip install hrv-analysis
you can also clone the repository:
$ git clone https://github.com/Aura-healthcare/hrv-analysis.git
$ cd hrv-analysis
$ uv sync --all-extras
#### Dependencies
**hrvanalysis** requires the following:
- Python (>= 3.5)
- astropy >= 3.0.4
- future >= 0.16.0
- nolds >= 0.4.1
- numpy >= 1.15.1
- scipy >= 1.1.0
Note: The package can be used with all Python versions from 3.5 to latest version (currently Python 3.9).
## Getting started
### Outliers and ectopic beats filtering methods
This package provides methods to remove outliers and ectopic beats from signal for further analysis. Those methods are useful to get Normal to Normal Interval (NN-intervals) from RR-intervals.
Please use this methods carefully as they might have a huge impact on features calculation.
```python
from hrvanalysis import remove_outliers, remove_ectopic_beats, interpolate_nan_values
# rr_intervals_list contains integer values of RR-interval
rr_intervals_list = [1000, 1050, 1020, 1080, ..., 1100, 1110, 1060]
# This remove outliers from signal
rr_intervals_without_outliers = remove_outliers(rr_intervals=rr_intervals_list,
low_rri=300, high_rri=2000)
# This replace outliers nan values with linear interpolation
interpolated_rr_intervals = interpolate_nan_values(rr_intervals=rr_intervals_without_outliers,
interpolation_method="linear")
# This remove ectopic beats from signal
nn_intervals_list = remove_ectopic_beats(rr_intervals=interpolated_rr_intervals, method="malik")
# This replace ectopic beats nan values with linear interpolation
interpolated_nn_intervals = interpolate_nan_values(rr_intervals=nn_intervals_list)
```
You can find how to use the following methods, references and more details in the [documentation](https://aura-healthcare.github.io/hrv-analysis/tutorial.html):
- remove_outliers
- remove_ectopic_beats
### Features calculation
There are 4 types of features you can get from NN-intervals:
> Time domain features : **Mean_NNI, SDNN, SDSD, NN50, pNN50, NN20, pNN20, RMSSD, Median_NN, Range_NN, CVSD, CV_NNI, Mean_HR, Max_HR, Min_HR, STD_HR**
> Geometrical domain features : **Triangular_index, TINN**
> Frequency domain features : **LF, HF, VLF, LH/HF ratio, LFnu, HFnu, Total_Power**
> Non Linear domain features : **CSI, CVI, Modified_CSI, SD1, SD2, SD1/SD2 ratio, SampEn**
As an exemple, what you can compute to get Time domain analysis is :
```python
from hrvanalysis import get_time_domain_features
# nn_intervals_list contains integer values of NN-interval
nn_intervals_list = [1000, 1050, 1020, 1080, ..., 1100, 1110, 1060]
time_domain_features = get_time_domain_features(nn_intervals_list)
>>> time_domain_features
{'mean_nni': 718.248,
'sdnn': 43.113,
'sdsd': 19.519,
'nni_50': 24,
'pnni_50': 2.4,
'nni_20': 225,
'pnni_20': 22.5,
'rmssd': 19.519,
'median_nni': 722.5,
'range_nni': 249,
'cvsd': 0.0272,
'cvnni': 0.060,
'mean_hr': 83.847,
'max_hr': 101.694,
'min_hr': 71.513,
'std_hr': 5.196}
```
You can find how to use the following methods, references and details about each feature in the [documentation](https://aura-healthcare.github.io/hrv-analysis/tutorial.html):
- get_time_domain_features
- get_geometrical_features
- get_frequency_domain_features
- get_csi_cvi_features
- get_poincare_plot_features
- get_sampen
### Plot functions
There are several plot functions that allow you to see, for example, the Power Spectral Density (PSD) for frequency domain features or Poincaré Plot for non linear domain features:
```python
from hrvanalysis import plot_psd
# nn_intervals_list contains integer values of NN-interval
nn_intervals_list = [1000, 1050, 1020, 1080, ..., 1100, 1110, 1060]
plot_psd(nn_intervals_list, method="welch")
plot_psd(nn_intervals_list, method="lomb")
```
```python
from hrvanalysis import plot_poincare
# nn_intervals_list contains integer values of NN-interval
nn_intervals_list = [1000, 1050, 1020, 1080, ..., 1100, 1110, 1060]
plot_poincare(nn_intervals_list)
plot_poincare(nn_intervals_list, plot_sd_features=True)
```
You can find how to use methods and details in the [documentation](https://aura-healthcare.github.io/hrv-analysis/tutorial.html):
- plot_distrib
- plot_timeseries
- plot_psd
- plot_poincare
Here is a high level view of the distinct building blocks of the package:
## References
Here are the main references used to compute the set of features and for signal processing methods:
> Heart rate variability - Standards of measurement, physiological interpretation, and clinical use, Task Force of The European Society of Cardiology and The North American Society of Pacing and Electrophysiology, 1996
> Signal Processing Methods for Heart Rate Variability - Gari D. Clifford, 2002
> Physiological time-series analysis using approximate entropy and sample entropy, Joshua S. Richman, J. Randall Moorman - 2000
> Using Lorenz plot and Cardiac Sympathetic Index of heart rate variability for detecting seizures for patients with epilepsy, Jesper Jeppesen et al, 2014
## Authors
**Robin Champseix** - (https://github.com/robinchampseix)
## License
This project is licensed under the *GNU GENERAL PUBLIC License* - see the [LICENSE.md](https://github.com/Aura-healthcare/hrv-analysis/blob/master/LICENSE) file for details
## How to contribute
Please refer to [How To Contribute](https://github.com/Aura-healthcare/hrv-analysis/blob/master/CONTRIBUTING.md)
## Acknowledgments
I hereby thank Laurent Ribière and Clément Le Couedic, my coworkers who gave me time to Open Source this library.
I also thank Fabien Arcellier for his advices on to how build a library in PyPi.