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.. image:: https://github.com/embodied-computation-group/systole/blob/dev/docs/source/images/logo.png :align: center
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Systole is an open-source Python package implementing simple tools for working with cardiac signals for psychophysiology research. In particular, the package provides tools to pre-process, visualize, and analyze cardiac data. This includes tools for data epoching, artefact detection, artefact correction, evoked heart rate analyses, heart rate variability analyses, circular statistical approaches to analysing cardiac cycles, and synchronising stimulus presentation with different cardiac phases via Psychopy.
The documentation can be found under the following link <https://embodied-computation-group.github.io/systole/#>
_.
If you have questions, you can ask them in the Gitter chat <https://gitter.im/ecg-systole/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge>
_.
How to cite? ++++++++++++
If you are using Systole in a publication we ask you to cite the following paper::
Legrand, N. & Allen, M., (2022). Systole: A python package for cardiac signal synchrony and analysis. Journal of Open Source Software, 7(69), 3832, https://doi.org/10.21105/joss.03832
Installation ++++++++++++
Systole can be installed using pip:
.. code-block:: shell
pip install systole
The following packages are required to use Systole:
Numpy <https://numpy.org/>
_ (>=1.15)SciPy <https://www.scipy.org/>
_ (>=1.3.0)Pandas <https://pandas.pydata.org/>
_ (>=0.24)Numba <http://numba.pydata.org/>
_ (>=0.51.2)Seaborn <https://seaborn.pydata.org/>
_ (>=0.9.0)Matplotlib <https://matplotlib.org/>
_ (>=3.0.2)Bokeh <https://docs.bokeh.org/en/latest/index.html#>
_ (>=2.3.3)pyserial <https://pyserial.readthedocs.io/en/latest/pyserial.html>
_ (>=3.4)setuptools <https://setuptools.pypa.io/en/latest/>
_ (>=38.4)requests <https://docs.python-requests.org/en/latest/>
_ (>=2.26.0)tabulate <https://github.com/astanin/python-tabulate/>
_ (>=0.8.9)The Python version should be 3.7 or higher.
For an introduction to Systole and cardiac signal analysis, you can refer to the following tutorial:
.. list-table:: :widths: 60 40 :header-rows: 0 :align: center
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.. |Colab badge 4| image:: https://colab.research.google.com/assets/colab-badge.svg :target: https://colab.research.google.com/github/embodied-computation-group/systole/blob/dev/source/notebooks/4-HeartRateVariability.ipynb
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Getting started +++++++++++++++
.. code-block:: python
from systole import import_dataset1
signal = import_dataset1(modalities=['ECG']).ecg.to_numpy()
The package integrates a set of functions for interactive or non interactive data visualization based on Matplotlib <https://matplotlib.org/>
and Bokeh <https://docs.bokeh.org/en/latest/index.html#>
.
.. code-block:: python
from systole.plots import plot_raw
plot_raw(signal[60000 : 120000], modality="ecg", backend="bokeh", show_heart_rate=True, show_artefacts=True, figsize=300)
.. image:: https://github.com/embodied-computation-group/systole/blob/dev/docs/source/images/raw.png :align: center
Artefacts can be detected and corrected in the RR interval time series or the peaks vector using the method proposed by Lipponen & Tarvainen (2019).
.. code-block:: python
from systole.detection import ecg_peaks from systole.plots import plot_subspaces
signal, peaks = ecg_peaks(signal, method='pan-tompkins', sfreq=1000)
plot_subspaces(peaks, input_type="peaks", backend="bokeh")
.. image:: https://github.com/embodied-computation-group/systole/blob/dev/docs/source/images/subspaces.png :align: center
Systole implements time-domain, frequency-domain and non-linear HRV indices, as well as tools for evoked heart rate analysis.
.. code-block:: python
from bokeh.layouts import row from systole.plots import plot_frequency, plot_poincare
row( plot_frequency(peaks, input_type="peaks", backend="bokeh", figsize=(300, 200)), plot_poincare(peaks, input_type="peaks", backend="bokeh", figsize=(200, 200)), )
.. image:: https://github.com/embodied-computation-group/systole/blob/dev/docs/source/images/hrv.png :align: center
The package natively supports recording of physiological signals from the following setups:
Nonin 3012LP Xpod USB pulse oximeter <https://www.nonin.com/products/xpod/>
together with the Nonin 8000SM 'soft-clip' fingertip sensors <https://www.nonin.com/products/8000s/>
(USB).Brain product ExG amplifier <https://www.brainproducts.com/>
_ (Ethernet)... code-block:: python
from systole.viewer import Viewer
view = Viewer( input_folder="/BIDS/folder/path/", pattern="task-mytask", modality="beh", signal_type="ECG" )
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.. image:: https://github.com/embodied-computation-group/systole/blob/dev/docs/source/images/peaks.gif :align: center
.. image:: https://github.com/embodied-computation-group/systole/blob/dev/docs/source/images/segments.gif :align: center
Development +++++++++++
This module was created and is maintained by Nicolas Legrand and Micah Allen (ECG group, https://the-ecg.org/). If you want to contribute, feel free to contact one of the developers, open an issue or submit a pull request.
This program is provided with NO WARRANTY OF ANY KIND.
Acknowledgements ++++++++++++++++
This software and the ECG are supported by a Lundbeckfonden Fellowship (R272-2017-4345), and the AIAS-COFUND II fellowship programme that is supported by the Marie Skłodowska-Curie actions under the European Union’s Horizon 2020 (Grant agreement no 754513), and the Aarhus University Research Foundation.
Systole was largely inspired by pre-existing toolboxes dedicated to heartrate variability and signal analysis.
HeartPy: https://python-heart-rate-analysis-toolkit.readthedocs.io/en/latest/
ECG-detector: https://github.com/berndporr/py-ecg-detectors
Pingouin: https://pingouin-stats.org/
NeuroKit2: https://github.com/neuropsychology/NeuroKit
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|AU| |lundbeck| |lab|
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