CardIO是一个用于心脏信号数据科学研究的图书馆__Jupyter Notebook_下载.zip

# 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} } ```