ECG-featurizer
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 [1] and NeuroKit2 [2].
Installation
To install ECG-featurizer, run this command in your terminal:
pip install ECG-featurizerDocumentation:
Featurize .dat-files:
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:
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] [3]
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:
Other examples:
Contributing
Citation:
Citation guidelines will come
Popularity:
References:
1 maintainer
Metadata
1 maintainer
Release files for ECG-featurizer 1.0.7
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| ECG-featurizer-1.0.7.tar.gz | 129.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| ECG_featurizer-1.0.7-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 148.1 kB
Release files / ECG-featurizer-1.0.7.tar.gz
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Release files / ECG_featurizer-1.0.7-py3-none-any.whl
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