ECG_QC (Quality Classification)
Full Documentation : https://aura-healthcare.github.io/ecg_qc/
Website : https://www.aura.healthcare
Github : https://github.com/Aura-healthcare
Version : 1.0b6
ecg_qc is a python library that classifies ECG signal into good/bad quality thanks to Machine Learning.
There are currently 4 trained models:
| Name | Type of model | (training) ECG time window | (training) ECG segments individual standardization |
|---|---|---|---|
| dfc_2s.pkl | Decision Tree Classifier | 2 seconds | No |
| rfc_2s.pkl | Random Forest Classifier | 2 seconds | No |
| rfc_2s_norm.pkl | Random Forest Classifier | 2 seconds | Yes |
| xgb_9s.joblib | XGboost Classifier | 9 seconds | No |
Installation / Prerequisites
Dependencies
ecg_qc requires:
- Python (>= 3.6)
- biosppy>=0.6.1
- dill>=0.3.4
- 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 EcgQcClass initialization:
ecg_qc = EcgQc()Default parameters:
ecg_qc = EcgQc(model='rfc_norm_2s.pkl',
sampling_frequency=256,
normalized=True)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)Computing SQIs before making prediction:
ecg_data = [1905.72, ... -150.75995323, -134.14559104] # ECG values with same sampling frequency as class declaration
sqi_scores = ecg_qc.compute_sqi_score(ecg_data)
signal_quality = ecg_qc.predict_quality(sqi_scores)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
References
Nemcova, A., Smisek, R., Opravilová, K., Vitek, M., Smital, L., & Maršánová, L. (2020). Brno University of Technology ECG Quality Database (BUT QDB) (version 1.0.0). PhysioNet. https://doi.org/10.13026/kah4-0w24.
