Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/PIA-Group/BioSPPy
Biosignal Processing in Python
https://github.com/PIA-Group/BioSPPy
biosignals data-science physiological-computing python signal-processing
Last synced: 6 days ago
JSON representation
Biosignal Processing in Python
- Host: GitHub
- URL: https://github.com/PIA-Group/BioSPPy
- Owner: PIA-Group
- License: other
- Archived: true
- Created: 2015-07-24T16:57:31.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2022-08-02T13:35:18.000Z (almost 2 years ago)
- Last Synced: 2024-03-07T09:34:23.938Z (4 months ago)
- Topics: biosignals, data-science, physiological-computing, python, signal-processing
- Language: Python
- Size: 2.51 MB
- Stars: 554
- Watchers: 34
- Forks: 269
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Lists
- awesome-bci - BioSPPy
- awesome-bci - BioSPPy
- my_awesome-bci - BioSPPy
README
> This repository is archived. The BioSPPy toolbox is now maintained at [scientisst/BioSPPy](https://github.com/scientisst/BioSPPy).
# BioSPPy - Biosignal Processing in Python
*A toolbox for biosignal processing written in Python.*
[](http://biosppy.readthedocs.org/)
The toolbox bundles together various signal processing and pattern recognition
methods geared towards the analysis of biosignals.
Highlights:
- Support for various biosignals: BVP, ECG, EDA, EEG, EMG, PCG, PPG, Respiration
- Signal analysis primitives: filtering, frequency analysis
- Clustering
- Biometrics
Documentation can be found at:
## Installation
Installation can be easily done with `pip`:
```bash
$ pip install biosppy
```
## Simple Example
The code below loads an ECG signal from the `examples` folder, filters it,
performs R-peak detection, and computes the instantaneous heart rate.
```python
from biosppy import storage
from biosppy.signals import ecg
# load raw ECG signal
signal, mdata = storage.load_txt('./examples/ecg.txt')
# process it and plot
out = ecg.ecg(signal=signal, sampling_rate=1000., show=True)
```
This should produce a plot similar to the one below.
[]()
## Dependencies
- bidict
- h5py
- matplotlib
- numpy
- scikit-learn
- scipy
- shortuuid
- six
- joblib
## Citing
Please use the following if you need to cite BioSPPy:
- Carreiras C, Alves AP, Lourenço A, Canento F, Silva H, Fred A, *et al.*
**BioSPPy - Biosignal Processing in Python**, 2015-,
https://github.com/PIA-Group/BioSPPy/ [Online; accessed ```--```].
```latex
@Misc{,
author = {Carlos Carreiras and Ana Priscila Alves and Andr\'{e} Louren\c{c}o and Filipe Canento and Hugo Silva and Ana Fred and others},
title = {{BioSPPy}: Biosignal Processing in {Python}},
year = {2015--},
url = "https://github.com/PIA-Group/BioSPPy/",
note = {[Online; accessed ]}
}
```
## License
BioSPPy is released under the BSD 3-clause license. See LICENSE for more details.
## Disclaimer
This program is distributed in the hope it will be useful and provided
to you "as is", but WITHOUT ANY WARRANTY, without even the implied
warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. This
program is NOT intended for medical diagnosis. We expressly disclaim any
liability whatsoever for any direct, indirect, consequential, incidental
or special damages, including, without limitation, lost revenues, lost
profits, losses resulting from business interruption or loss of data,
regardless of the form of action or legal theory under which the
liability may be asserted, even if advised of the possibility of such
damages.