Fairseq-signals is a collection of deep learning models for ECG data processing based on the fairseq.

We provide implementations of various deep learning methods on ECG data, including official implementations of our works.

• Multi-Modal Masked Autoencoders for Medical Vision-and-Language Pre-Training
• Multi-modal Understanding and Generation for Medical Images and Text via Vision-Language Pre-Training
• Lead-agnostic Self-supervised Learning for Local and Global Representations of Electrocardiogram*
• 3KG: Contrastive Learning of 12-Lead Electrocardiograms using Physiologically-Inspired Augmentations
• CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients
• wav2vec 2.0: A Framework for Self-supervised Learning of Speech Representations
• A Simple Framework for Contrastive Learning of Visual Representations

* denotes for an official implementation

We will keep implementing new methods in this repo. If you have any recommendations, please contact us via an issue or an e-mail.

• PyTorch version >= 1.5.0
• Python version >= 3.6, and <= 3.9
• For training new models, you'll also need an NVIDIA GPU and NCCL
• To install fairseq-signals from source and develop locally:

git clone https://github.com/Jwoo5/fairseq-signals
cd fairseq-signals
pip install --editable ./

• To preprocess ECG datasets: pip install scipy wfdb
• To build cython components: python setup.py build_ext --inplace
• For large datasets install PyArrow: pip install pyarrow

We provide pre-processing codes for various ECG datasets.

• PhysioNet2021
• PTB-XL

Given a directory that contains WFDB directories to be pre-processed for PhysioNet2021:

$ python fairseq_signals/data/ecg/preprocess/preprocess_physionet2021.py \
    /path/to/physionet2021/ \
    --dest /path/to/output \
    --workers $N

Given a directory that contains .dat files from PTB-XL:

$ python fairseq_signals/data/ecg/preprocess/preprocess_ptbxl.py \
    /path/to/ptbxl/records500/ \
    --dest /path/to/output

Given a directory that contains pre-processed data:

$ python fairseq_signals/data/ecg/preprocess/manifest.py \
    /path/to/data/ \
    --dest /path/to/manifest \
    --valid-percent $valid

For patient identification:

$ python fairseq_signals/data/ecg/preprocess/manifest_identification.py \
    /path/to/data \
    --dest /path/to/manifest \
    --valid-percent $valid

Please fine more details about pre-processing and data manifest from here.

We provide pre-processing codes for the following datasets.

• PTB-XL
• MIMIC-IV-ECG
• ECG-QA

For multi-modal pre-training of ECGs with reports using the PTB-XL dataset:

$ python fairseq_signals/data/ecg_text/preprocess/preprocess_ptbxl.py \
   /path/to/ptbxl \
   --dest /path/to/output \

For multi-modal pre-training of ECGs with reports using the MIMIC-IV-ECG dataset:

$ python fairseq_signals/data/ecg_text/preprocess/preprocess_mimic_iv_ecg.py \
   /path/to/mimic-iv-ecg \
   --dest /path/to/output \

For ECG Question Answering task with the ECG-QA dataset:

• Map ecg_id to the corresponding ECG file path (you can find these scripts in the ECG-QA repository)
  • For PTB-XL-based ECG-QA:

    $ python mapping_ptbxl_samples.py ecgqa/ptbxl \
        --ptbxl-data-dir $ptbxl_dir \
        --dest $dest_dir

  • For MIMIC-IV-ECG-based ECG-QA:

    $ python mapping_mimic_iv_ecg_samples.py ecgqa/mimic-iv-ecg \
        --mimic-iv-ecg-data-dir $mimic_iv_ecg_dir \
        --dest $dest_dir

• Preprocess ECG-QA and prepare manifests

  $ fairseq_signals/data/ecg_text/preprocess/preprocess_ecgqa.py /path/to/ecgqa \
      --dest /path/to/output \
      --apply_paraphrase

You don't need to run additional scripts to prepare manifest files for ECG-QA dataset since it automatically generates manifest files during the pre-processing process.

Given a directory that contains pre-processed PTB-XL data:

$ python fairseq_signals/data/ecg_text/preprocess/manifest.py \
    /path/to/data \
    --dest /path/to/manifest \
    --valid-percent $valid

Please find more details about pre-processing and data manifest here.

We provide detailed READMEs for each model implementation:

• Multi-Modal Masked Autoencoders for Medical Vision-and-Language Pre-Training
• Multi-modal Understanding and Generation for Medical Images and Text via Vision-Language Pre-Training
• Lead-agnostic Self-supervised Learning for Local and Global Representations of Electrocardiogram*
• 3KG: Contrastive Learning of 12-Lead Electrocardiograms using Physiologically-Inspired Augmentations
• CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients
• wav2vec 2.0: A Framework for Self-supervised Learning of Speech Representations
• A Simple Framework for Contrastive Learning of Visual Representations

* denotes for an official implementation

If you have any questions or recommendations, please contact us via an issue or an e-mail.

• [email protected]