Fairseq-signals

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.

List of implemented papers:

• 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.

Requirements and Installation

• PyTorch version >= 1.5.0
• Python version >= 3.6
• 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

Getting Started

For uni-modal tasks (ECG Classification, ...)

Prepare ECG dataset

We provide pre-processing codes for various ECG datasets.

• PhysioNet2021
• PTB-XL

Pre-process

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

Prepare data manifest

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.

For multi-modal tasks (Multi-modal pre-training or ECG question answering)

Prepare ECG dataset

We provide pre-processing codes for the following datasets.

• PTB-XL
• ECG-QA

Pre-process

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

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

For ECG Question Answering task:

$ python fairseq_signals/data/ecg_text/preprocess/preprocess_ecgqa.py \
    /path/to/ecgqa \
    --ptbxl-data-dir /path/to/ptbxl \
    --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.

Prepare data manifest

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

Examples

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

Contact

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

• ojw0123@kaist.ac.kr