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].
To install ECG-featurizer, run this command in your terminal:
pip install ECG-featurizer
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)
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)
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
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)
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/"
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
Citation guidelines will come
[1] | WFDB: https://github.com/MIT-LCP/wfdb-python |
[2] | 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 |
[3] | 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; https://circ.ahajournals.org/content/101/23/e215.full]; 2000 (June 13). PMID: 10851218; doi: 10.1161/01.CIR.101.23.e215 |