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Nicolas Legrand
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‎source/auto_examples/auto_examples_jupyter.zip

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‎source/auto_examples/auto_examples_python.zip

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‎source/auto_examples/index.rst

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@@ -62,7 +62,7 @@ If you want to see the tutorials in action, you can also click on the link below
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.. raw:: html
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<div class="sphx-glr-thumbcontainer" tooltip="This example shows how to detect extra ectobeats, missed and ectobeats from RR time series usin...">
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<div class="sphx-glr-thumbcontainer" tooltip="Here, we describe two method for artefacts and outliers correction, after detection using the m...">
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.. only:: html
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‎source/auto_examples/plot_ArtefactsCorrection.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\nOutliers and ectobeats correction\n=================================\n\nThis example shows how to detect extra ectobeats, missed and ectobeats from RR\ntime series using the method proposed by Lipponen & Tarvainen (2019) [#]_.\n"
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"\nOutliers and ectobeats correction\n=================================\n\nHere, we describe two method for artefacts and outliers correction, after\ndetection using the method proposed by Lipponen & Tarvainen (2019) [#]_.\n"
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]
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},
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{
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},
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"outputs": [],
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"source": [
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"# Author: Nicolas Legrand <nicolas.legrand@cfin.au.dk>\n# Licence: GPL v3\n\n# Two approaches for artefacts correction are proposed:\n# * `correct_peaks()` will find and correct artefacts in a boolean peaks\n# vector, thus ensuring the length of recording remain constant and corrected\n# peaks fit the signal sampling rate. This method is more adapted to\n# event-related cardiac activity designs.\n\n# * `correct_rr()` will find and correct artefacts in the RR time series. The\n# signal length will possibly change after the interpolation of long, short or\n# ectopic beats. This method is more relevant for HRV analyse of long recording\n# where the timing of experimental events is not important.\n#\n# import numpy as np\n# import matplotlib.pyplot as plt\n#\n# rr = rr_artifacts()\n#\n# # Method 1\n# ##########"
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"# Author: Nicolas Legrand <nicolas.legrand@cfin.au.dk>\n# Licence: GPL v3\n\n# Two approaches for artefacts correction are proposed:\n# * `correct_peaks()` will find and correct artefacts in a boolean peaks\n# vector, thus ensuring the length of recording remain constant and corrected\n# peaks fit the signal sampling rate. This method is more adapted to\n# event-related cardiac activity designs.\n\n# * `correct_rr()` will find and correct artefacts in the RR time series. The\n# signal length will possibly change after the interpolation of long, short or\n# ectopic beats. This method is more relevant for HRV analyse of long recording\n# where the timing of experimental events is not important."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import numpy as np\nimport matplotlib.pyplot as plt\nfrom systole import simulate_rr\nfrom systole.plotting import plot_subspaces\nfrom systole.correction import correct_peaks, correct_rr"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#############################\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"peaks = simulate_rr(as_peaks=True)\npeaks_correction = correct_peaks(peaks)\npeaks_correction"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#############################\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"rr = simulate_rr()\nrr_correction = correct_rr(rr)"
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},
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{

‎source/auto_examples/plot_ArtefactsCorrection.py

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Outliers and ectobeats correction
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=================================
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This example shows how to detect extra ectobeats, missed and ectobeats from RR
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time series using the method proposed by Lipponen & Tarvainen (2019) [#]_.
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Here, we describe two method for artefacts and outliers correction, after
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detection using the method proposed by Lipponen & Tarvainen (2019) [#]_.
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"""
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# Author: Nicolas Legrand <nicolas.legrand@cfin.au.dk>
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# signal length will possibly change after the interpolation of long, short or
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# ectopic beats. This method is more relevant for HRV analyse of long recording
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# where the timing of experimental events is not important.
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#
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# import numpy as np
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# import matplotlib.pyplot as plt
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#
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# rr = rr_artifacts()
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#
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# # Method 1
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# ##########
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#%%
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import numpy as np
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import matplotlib.pyplot as plt
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from systole import simulate_rr
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from systole.plotting import plot_subspaces
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from systole.correction import correct_peaks, correct_rr
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#%% Method 1 - Peaks correction
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# #############################
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peaks = simulate_rr(as_peaks=True)
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peaks_correction = correct_peaks(peaks)
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peaks_correction
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#%% Method 2 - RR correction
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# #############################
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rr = simulate_rr()
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rr_correction = correct_rr(rr)
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#%%
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# References

‎source/auto_examples/plot_ArtefactsCorrection.py.md5

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‎source/auto_examples/plot_ArtefactsCorrection.rst

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.. note::
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:class: sphx-glr-download-link-note
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.. only:: html
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.. note::
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:class: sphx-glr-download-link-note
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Click :ref:`here <sphx_glr_download_auto_examples_plot_ArtefactsCorrection.py>` to download the full example code
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.. rst-class:: sphx-glr-example-title
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Click :ref:`here <sphx_glr_download_auto_examples_plot_ArtefactsCorrection.py>` to download the full example code
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.. rst-class:: sphx-glr-example-title
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.. _sphx_glr_auto_examples_plot_ArtefactsCorrection.py:
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.. _sphx_glr_auto_examples_plot_ArtefactsCorrection.py:
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Outliers and ectobeats correction
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=================================
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This example shows how to detect extra ectobeats, missed and ectobeats from RR
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time series using the method proposed by Lipponen & Tarvainen (2019) [#]_.
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Here, we describe two method for artefacts and outliers correction, after
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detection using the method proposed by Lipponen & Tarvainen (2019) [#]_.
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.. code-block:: default
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# signal length will possibly change after the interpolation of long, short or
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# ectopic beats. This method is more relevant for HRV analyse of long recording
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# where the timing of experimental events is not important.
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#
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# import numpy as np
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# import matplotlib.pyplot as plt
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#
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# rr = rr_artifacts()
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#
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# # Method 1
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# ##########
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.. code-block:: default
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import numpy as np
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import matplotlib.pyplot as plt
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from systole import simulate_rr
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from systole.plotting import plot_subspaces
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from systole.correction import correct_peaks, correct_rr
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#############################
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.. code-block:: default
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peaks = simulate_rr(as_peaks=True)
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peaks_correction = correct_peaks(peaks)
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peaks_correction
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.. rst-class:: sphx-glr-script-out
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Out:
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.. code-block:: none
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{'clean_peaks': array([ True, False, False, ..., False, False, False]), 'ectopic': 0, 'short': 0, 'long': 0, 'extra': 1, 'missed': 1}
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#############################
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.. code-block:: default
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rr = simulate_rr()
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rr_correction = correct_rr(rr)
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 0 minutes 0.002 seconds)
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**Total running time of the script:** ( 0 minutes 0.493 seconds)
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.. _sphx_glr_download_auto_examples_plot_ArtefactsCorrection.py:
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.. container:: sphx-glr-download
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.. container:: sphx-glr-download sphx-glr-download-python
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:download:`Download Python source code: plot_ArtefactsCorrection.py <plot_ArtefactsCorrection.py>`
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.. container:: sphx-glr-download
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.. container:: sphx-glr-download sphx-glr-download-jupyter
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:download:`Download Jupyter notebook: plot_ArtefactsCorrection.ipynb <plot_ArtefactsCorrection.ipynb>`
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‎source/auto_examples/plot_ArtefactsCorrection_codeobj.pickle

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‎source/auto_examples/sg_execution_times.rst

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Computation times
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=================
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**00:36.528** total execution time for **auto_examples** files:
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**00:00.493** total execution time for **auto_examples** files:
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| :ref:`sphx_glr_auto_examples_plot_HeartBeatEvokedArpeggios.py` (``plot_HeartBeatEvokedArpeggios.py``) | 00:36.528 | 0.0 MB |
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+---------------------------------------------------------------------------------------------------------+-----------+--------+
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| :ref:`sphx_glr_auto_examples_plot_ArtefactsCorrection.py` (``plot_ArtefactsCorrection.py``) | 00:00.000 | 0.0 MB |
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| :ref:`sphx_glr_auto_examples_plot_ArtefactsCorrection.py` (``plot_ArtefactsCorrection.py``) | 00:00.493 | 0.0 MB |
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| :ref:`sphx_glr_auto_examples_plot_ArtefactsDetection.py` (``plot_ArtefactsDetection.py``) | 00:00.000 | 0.0 MB |
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+---------------------------------------------------------------------------------------------------------+-----------+--------+
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| :ref:`sphx_glr_auto_examples_plot_HeartBeatEvokedArpeggios.py` (``plot_HeartBeatEvokedArpeggios.py``) | 00:00.000 | 0.0 MB |
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+---------------------------------------------------------------------------------------------------------+-----------+--------+
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| :ref:`sphx_glr_auto_examples_plot_InstantaneousHeartRate.py` (``plot_InstantaneousHeartRate.py``) | 00:00.000 | 0.0 MB |
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+---------------------------------------------------------------------------------------------------------+-----------+--------+
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| :ref:`sphx_glr_auto_examples_plot_InteractiveVisualizations.py` (``plot_InteractiveVisualizations.py``) | 00:00.000 | 0.0 MB |

‎systole/datasets/__pycache__/__init__.cpython-37.pyc

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