CardIO is designed to build end-to-end machine learning models for deep research of electrocardiograms.
Main features:
- load and save signals in various formats: WFDB, DICOM, EDF, XML (Schiller), etc.
- resample, crop, flip and filter signals
- detect PQ, QT, QRS segments
- calculate heart rate and other ECG characteristics
- perform complex processing like fourier and wavelet transformations
- apply custom functions to the data
- recognize heart diseases (e.g. atrial fibrillation)
- efficiently work with large datasets that do not even fit into memory
- perform end-to-end ECG processing
- build, train and test neural networks and other machine learning models
For more details see the documentation and tutorials.
CardIO is based on BatchFlow. You might benefit from reading its documentation. However, it is not required, especially at the beginning.
CardIO has three modules: core,
models and
pipelines.
core module contains EcgBatch and EcgDataset classes.
EcgBatch defines how ECGs are stored and includes actions for ECG processing. These actions might be used to build multi-staged workflows that can also involve machine learning models. EcgDataset is a class that stores indices of ECGs and generates batches of type EcgBatch.
models module provides several ready to use models for important problems in ECG analysis:
- how to detect specific features of ECG like R-peaks, P-wave, T-wave, etc
- how to recognize heart diseases from ECG, for example, atrial fibrillation
pipelines module contains predefined workflows to
- train a model and perform an inference to detect PQ, QT, QRS segments and calculate heart rate
- train a model and perform an inference to find probabilities of heart diseases, in particular, atrial fibrillation
Here is an example of a pipeline that loads ECG signals, makes preprocessing and trains a model for 50 epochs:
train_pipeline = (
ds.Pipeline()
.init_model("dynamic", DirichletModel, name="dirichlet", config=model_config)
.init_variable("loss_history", init_on_each_run=list)
.load(components=["signal", "meta"], fmt="wfdb")
.load(components="target", fmt="csv", src=LABELS_PATH)
.drop_labels(["~"])
.rename_labels({"N": "NO", "O": "NO"})
.flip_signals()
.random_resample_signals("normal", loc=300, scale=10)
.random_split_signals(2048, {"A": 9, "NO": 3})
.binarize_labels()
.train_model("dirichlet", make_data=concatenate_ecg_batch, fetches="loss", save_to=V("loss_history"), mode="a")
.run(batch_size=100, shuffle=True, drop_last=True, n_epochs=50)
)
CardIOmodule is in the beta stage. Your suggestions and improvements are very welcome.
CardIOsupports python 3.5 or higher.
With pipenv:
pipenv install git+https://github.com/analysiscenter/cardio.git#egg=cardio
With pip:
pip3 install git+https://github.com/analysiscenter/cardio.git
After that just import cardio:
import cardioWhen cloning repo from GitHub use flag --recursive to make sure that batchflow submodule is also cloned.
git clone --recursive https://github.com/analysiscenter/cardio.git
Please cite CardIO in your publications if it helps your research.
Khudorozhkov R., Illarionov E., Kuvaev A., Podvyaznikov D. CardIO library for deep research of heart signals. 2017.
@misc{cardio_2017_1156085,
author = {R. Khudorozhkov and E. Illarionov and A. Kuvaev and D. Podvyaznikov},
title = {CardIO library for deep research of heart signals},
year = 2017,
doi = {10.5281/zenodo.1156085},
url = {https://doi.org/10.5281/zenodo.1156085}
}
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