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ECG-featurizer-1.0.7


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توضیحات

This Python package recognize patterns in an ECG and extract features
ویژگی مقدار
سیستم عامل -
نام فایل ECG-featurizer-1.0.7
نام ECG-featurizer
نسخه کتابخانه 1.0.7
نگهدارنده []
ایمیل نگهدارنده []
نویسنده Bjørn-Jostein Singstad
ایمیل نویسنده bjorn_sing@hotmail.com
آدرس صفحه اصلی https://github.com/ECG-featurizer/ECG-featurizer
آدرس اینترنتی https://pypi.org/project/ECG-featurizer/
مجوز -
************** ECG-featurizer ************** .. image:: /docs/source/img/ECG-featurizer_banner.png 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 [#]_. .. image:: https://readthedocs.org/projects/ECG-featurizer/badge/?version=latest :target: https://ECG-featurizer.readthedocs.io/en/latest/?badge=latest :alt: Documentation Status .. image:: https://travis-ci.org/ECG-featurizer/ECG-featurizer.svg?branch=master :target: https://travis-ci.org/ECG-featurizer/ECG-featurizer .. image:: https://coveralls.io/repos/github/ECG-featurizer/ECG-featurizer/badge.svg?branch=master :target: https://coveralls.io/github/ECG-featurizer/ECG-featurizer?branch=master .. image:: https://badge.fury.io/py/ECG-featurizer.svg :target: https://badge.fury.io/py/ECG-featurizer .. image:: https://pypip.in/d/ECG-featurizer/badge.svg :target: https://pypi.python.org/pypi/ECG-featurizer/ .. image:: https://img.shields.io/github/forks/ECG-featurizer/ECG-featurizer.svg :alt: GitHub Forks :target: https://github.com/ECG-featurizer/ECG-featurizer/network .. image:: https://img.shields.io/github/issues/ECG-featurizer/ECG-featurizer.svg :alt: GitHub Open Issues :target: https://github.com/ECG-featurizer/ECG-featurizer/issues .. image:: http://www.repostatus.org/badges/latest/active.svg :alt: Project Status: Active - The project has reached a stable, usable state and is being actively developed. :target: http://www.repostatus.org/#active .. image:: https://zenodo.org/badge/308713553.svg :target: https://zenodo.org/badge/latestdoi/308713553 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: ----------- .. image:: https://img.shields.io/pypi/dd/ECG-featurizer :target: https://pypi.python.org/pypi/ECG-featurizer .. image:: https://img.shields.io/github/stars/ECG-featurizer/ECG-featurizer :target: https://github.com/ECG-featurizer/ECG-featurizer/stargazers .. image:: https://img.shields.io/github/forks/ECG-featurizer/ECG-featurizer :target: https://github.com/ECG-featurizer/ECG-featurizer/network 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


نیازمندی

مقدار نام
>=0.0.41 neurokit2
>=1.19.0 numpy
>=3.1.1 wfdb
>=1.0.5 pandas


زبان مورد نیاز

مقدار نام
>=3.6 Python


نحوه نصب


نصب پکیج whl ECG-featurizer-1.0.7:

    pip install ECG-featurizer-1.0.7.whl


نصب پکیج tar.gz ECG-featurizer-1.0.7:

    pip install ECG-featurizer-1.0.7.tar.gz