a package to compute if ECG signal quality is optimal or noisy
Project description
ECG_QC (Quality Classification)
Website : https://www.aura.healthcare Github : https://github.com/Aura-healthcare Version : 1.0b3
Installation / Prerequisites
Dependencies
ecg_qc requires:
- Python (>= 3.6)
- biosppy>=0.6.1,
- pathtools>=0.1.2,
- py-ecg-detectors>=1.0.2
- scikit-learn>=0.23.2
- wfdb>=3.1.1
- xgboost>=1.3.1
User installation
The easiest way to install hrv-analysis is using pip
:
$ pip install ecg-qc
you can also clone the repository:
$ git clone https://github.com/Aura-healthcare/ecg_qc.git
$ python setup.py install
Getting started
Usage
Import:
from ecg_qc import ecg_qc
Class initialization:
ecg_qc = ecg_qc()
Default parameters
ecg_qc = ecg_qc(data_encoder='{}/ml/data_encoder/data_encoder.joblib'.format(
lib_path),
model='{}/ml/models/xgb.joblib'.format(lib_path),
sampling_frequency=1000))
Predicting the quality of the signal
ecg_data = [1905.72, ... -150.75995323, -134.14559104] # ECG values with same sampling frequency as class declaration
signal_quality = ecg_qc.get_signal_quality(ecg_data)
Authors
Alexandre CHIROUZE - (https://github.com/achirouze)
Alexis COMTE - (https://github.com/alexisgcomte)
Laura DUMONT - (https://github.com/laudmt)
License
This project is licensed under the GNU GENERAL PUBLIC License - see the LICENSE.md file for details
Acknowledgments
to complete
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
ecg_qc-1.0b3.tar.gz
(176.8 kB
view hashes)
Built Distribution
ecg_qc-1.0b3-py3-none-any.whl
(192.3 kB
view hashes)