Counting 3,384 Big Data & Machine Learning Frameworks, Toolsets, and Examples...
Suggestion? Feedback? Tweet @stkim1

Last Commit
Dec. 12, 2018
Nov. 5, 2017


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.

About CardIO

CardIO is based on Dataset. 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

Basic usage

Here is an example of a pipeline that loads ECG signals, makes preprocessing and trains a model for 50 epochs:

train_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)
    .rename_labels({"N": "NO", "O": "NO"})
    .random_resample_signals("normal", loc=300, scale=10)
    .random_split_signals(2048, {"A": 9, "NO": 3})
    .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)


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:

pipenv install git+

With pip:

pip3 install git+

After that just import cardio:

import cardio

Installation as a project repository

When cloning repo from GitHub use flag --recursive to make sure that Dataset submodule is also cloned.

git clone --recursive

Citing CardIO

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.
  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          = {}

Latest Releases
 Sep. 9 2018
More formats and actions
 Jan. 20 2018
Initial release
 Nov. 22 2017