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https://github.com/analysiscenter/cardio
CardIO is a library for data science research of heart signals
https://github.com/analysiscenter/cardio
data-science deep-learning deep-neural-networks healthcare machine-learning python
Last synced: about 1 month ago
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CardIO is a library for data science research of heart signals
- Host: GitHub
- URL: https://github.com/analysiscenter/cardio
- Owner: analysiscenter
- License: apache-2.0
- Created: 2017-11-05T21:37:17.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2024-06-17T22:38:29.000Z (6 months ago)
- Last Synced: 2024-08-03T21:03:36.016Z (4 months ago)
- Topics: data-science, deep-learning, deep-neural-networks, healthcare, machine-learning, python
- Language: Jupyter Notebook
- Homepage: https://analysiscenter.github.io/cardio/
- Size: 9.47 MB
- Stars: 248
- Watchers: 19
- Forks: 78
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
- awesome-healthmetrics - cardIO: data science research of heart signals in Python
README
# CardIO
`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](https://analysiscenter.github.io/cardio/).
## About CardIO
> CardIO is based on [BatchFlow](https://github.com/analysiscenter/batchflow). You might benefit from reading [its documentation](https://analysiscenter.github.io/batchflow).
However, it is not required, especially at the beginning.
CardIO has three modules: [``core``](https://analysiscenter.github.io/cardio/modules/core.html),
[``models``](https://analysiscenter.github.io/cardio/modules/models.html) and
[``pipelines``](https://analysiscenter.github.io/cardio/modules/pipelines.html).
``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
## Basic usage
Here is an example of a pipeline that loads ECG signals, makes preprocessing and trains a model for 50 epochs:
```python
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)
)
```
## Installation
> `CardIO` module is in the beta stage. Your suggestions and improvements are very welcome.
> `CardIO` supports python 3.5 or higher.
### Installation as a python package
With [pipenv](https://docs.pipenv.org/):
pipenv install git+https://github.com/analysiscenter/cardio.git#egg=cardio
With [pip](https://pip.pypa.io/en/stable/):
pip3 install git+https://github.com/analysiscenter/cardio.git
After that just import `cardio`:
```python
import cardio
```
### Installation as a project repository
When 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
## Citing CardIO
Please cite CardIO in your publications if it helps your research.
[](https://doi.org/10.5281/zenodo.1156085)
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}
}
```