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ECG-featurizer
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A method to extract features from electrocardiographic recordings
=================================================================
The purpose of this package is to make tabular data from ECG-recordings by calculating many features. The package is built on WFDB [#]_ and NeuroKit2 [#]_.
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Installation
-------------
To install ECG-featurizer, run this command in your terminal:
.. code-block::
pip install ECG-featurizer
Documentation:
--------------
Featurize .dat-files:
^^^^^^^^^^^^^^^^^^^^^
.. code-block:: python
from ECGfeaturizer import featurize as ef
# Make ECG-featurizer object
Feature_object =ef.get_features()
# Preprocess the data (filter, find peaks, etc.)
My_features=Feature_object.featurizer_dat(features=ecg_filenames,labels=labels,directory="./data/",demographical_data=demo_data)
Featurize .mat-files:
^^^^^^^^^^^^^^^^^^^^^
.. code-block:: python
from ECGfeaturizer import featurize as ef
number_of_ECGs = <the amount of ECGs>
directory = "<your dir>"
# Make ECG-featurizer object
Feature_object =ef.get_features()
# Preprocess the data (filter, find peaks, etc.)
My_features=Feature_object.featurizer_mat(num_features=number_of_ECGs, mat_dir = directory)
features:
^^^^^^^^^
A numpy array of ECG-recordings in directory. Each recording should have a file with the recording as a time series and one file with meta data containing information about the patient and measurement information. This is standard format for WFDB and PhysioNet-files [1]_ [#]_
**Supported input files:**
+-------------------+---------------------------+
| **Input data** | **Supported file format** |
+-------------------+---------------------------+
| ECG-recordings | .dat files |
+-------------------+---------------------------+
| Patient meta data | .hea files |
+-------------------+---------------------------+
labels:
^^^^^^^
A numpy array of labels / diagnoses for each ECG-recording. The length of the labels-array should have the same length as the features-array
.. code-block:: python
len(labels) == len(features)
directory:
^^^^^^^^^^
A string with the path to the features. If the folder structure looks like this:
| mypath
| ├── ECG-recordings
| │ ├── A0001.hea
| │ ├── A0001.dat
| │ ├── A0002.hea
| │ ├── A0002.dat
| │ └── Axxxx.dat
then the feature and directory varaible could be:
features[0]
"A0001"
directory
"./mypath/ECG-recordings/"
demographical_data:
^^^^^^^^^^^^^^^^^^^
The demographical data that is used in this function is *age* and *gender*. A Dataframe with the following 3 columns should be passed to the featurizer() function.
+---+---------+------------+-----------------+
| | **age** | **gender** | **filename_hr** |
+===+=========+============+=================+
| 0 | 11.0 | 1 | "A0001" |
+---+---------+------------+-----------------+
| 1 | 57.0 | 0 | "A0002" |
+---+---------+------------+-----------------+
| 2 | 94.0 | 0 | "A0003" |
+---+---------+------------+-----------------+
| 3 | 34.0 | 1 | "A0004" |
+---+---------+------------+-----------------+
The strings in the *filename_hr* -column should be the same as the strings in the feature array.
In this example gender is OneHot encoded such that
1 = Female
0 = Male
Tutorials:
----------
- `A tutorial will come <https://github.com/ECG-featurizer/ECG-featurizer/blob/main/docs/source/index.rst>`_
Other examples:
---------------
- `Some examples will come <https://github.com/ECG-featurizer/ECG-featurizer/blob/main/docs/source/index.rst>`_
Contributing
------------
|GPLv3 license|
.. |GPLv3 license| image:: https://img.shields.io/badge/License-GPLv3-blue.svg
:target: http://perso.crans.org/besson/LICENSE.html
Citation:
---------
**Citation guidelines will come**
Popularity:
-----------
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References:
-----------
.. [#] WFDB: https://github.com/MIT-LCP/wfdb-python
.. [#] Makowski, D., Pham, T., Lau, Z. J., Brammer, J. C., Lesspinasse, F., Pham, H.,
Schölzel, C., & S H Chen, A. (2020). NeuroKit2: A Python Toolbox for Neurophysiological
Signal Processing. Retrieved March 28, 2020, from https://github.com/neuropsychology/NeuroKit
.. [#] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23):e215-e220 [Circulation Electronic Pages; http://circ.ahajournals.org/content/101/23/e215.full]; 2000 (June 13). PMID: 10851218; doi: 10.1161/01.CIR.101.23.e215