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ECG-FM is a foundation model for electrocardiogram (ECG) analysis. Committed to open-source practices, ECG-FM was developed in collaboration with the [fairseq_signals](https://github.com/Jwoo5/fairseq-signals) framework, which implements a collection of deep learning methods for ECG analysis. This repository serves as a landing page and will host project-specific scripts as this work progresses.

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  <img src="docs/saliency.png" width="500">
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## News
- 2024-08-12: ECG-FM arxiv & GitHub released

## Model Details

ECG-FM adopts the wav2vec 2.0 architecture and was pretrained using the W2V+CMSC+RLM (WCR) method. It has 311,940,352 parameters and was trained using 4 NVIDIA A100 80GB GPUs over 16.5 days. For our transformer encoder, we selected hyperparameters consistent with a BERT-Large encoder. Further details are available in our [paper](https://arxiv.org/abs/2408.05178).

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  <img src="docs/architecture.png" width="750">
</div>

### Model Parameters
We are committed to open-weight practices. Model checkpoints have been made publicly available for [download on HuggingFace](https://huggingface.co/wanglab/ecg-fm-preprint).

Specifically, there is:

`mimic_iv_ecg_physionet_pretrained.pt`
- Was pretrained on [MIMIC-IV-ECG v1.0](https://physionet.org/content/mimic-iv-ecg/1.0/) and [PhysioNet 2021 v1.0.3](https://physionet.org/content/challenge-2021/1.0.3/).

`physionet_finetuned.pt`
- Was finetuned from `mimic_iv_ecg_physionet_pretrained.pt` on [PhysioNet 2021 v1.0.3](https://physionet.org/content/challenge-2021/1.0.3/).


**Disclaimer: These models are different from those reported in our arXiv paper.** These BERT-Base sized models were trained purely on public data sources due to privacy concerns surrounding UHN-ECG data and patient identification. Validation for the final models will be available upon full publication.

## Getting Started

### Installation
Clone [fairseq_signals](https://github.com/Jwoo5/fairseq-signals) and refer to the requirements and installation section in the top-level README.

### Data Preparation
We implemented a flexible, end-to-end, multi-source data preprocessing pipeline. Please refer to it [here](https://github.com/Jwoo5/fairseq-signals/tree/master/scripts/preprocess/ecg).

### Inference & Model Loading
See our [inference tutorial notebook](inference_tutorial.ipynb)!

### Training
Training is performed through the [fairseq_signals](https://github.com/Jwoo5/fairseq-signals) framework. To maximize reproducibility, we have provided [configuration files](https://huggingface.co/wanglab/ecg-fm-preprint).

Pretraining can be performed by downloading the `mimic_iv_ecg_physionet_pretrained.yaml` config (or modifying `fairseq-signals/examples/w2v_cmsc/config/pretraining/w2v_cmsc_rlm.yaml` as desired).
After modifying the relevant configuration file as desired, pretraining is performed using hydra's command line interface. This command highlights some popular config overrides:
```
FAIRSEQ_SIGNALS_ROOT="<TODO>"
MANIFEST_DIR="<TODO>/cmsc"
OUTPUT_DIR="<TODO>"

fairseq-hydra-train \
    task.data=$MANIFEST_DIR \
    dataset.valid_subset=valid \
    dataset.batch_size=64 \
    dataset.num_workers=10 \
    dataset.disable_validation=false \
    distributed_training.distributed_world_size=4 \
    optimization.update_freq=[2] \
    checkpoint.save_dir=$OUTPUT_DIR \
    checkpoint.save_interval=10 \
    checkpoint.keep_last_epochs=0 \
    common.log_format=csv \
    --config-dir $FAIRSEQ_SIGNALS_ROOT/examples/w2v_cmsc/config/pretraining \
    --config-name w2v_cmsc_rlm
```

Classification finetuning uses the `physionet_finetuned.yaml` or `fairseq-signals/examples/w2v_cmsc/config/finetuning/ecg_transformer/diagnosis.yaml` configs. This command highlights some popular config overrides:
```
FAIRSEQ_SIGNALS_ROOT="<TODO>"
PRETRAINED_MODEL="<TODO>"
MANIFEST_DIR="<TODO>"
LABEL_DIR="<TODO>"
OUTPUT_DIR="<TODO>"
NUM_LABELS=$(($(wc -l < "$LABEL_DIR/label_def.csv") - 1))
POS_WEIGHT=$(cat $LABEL_DIR/pos_weight.txt)

fairseq-hydra-train \
    task.data=$MANIFEST_DIR \
    model.model_path=$PRETRAINED_MODEL \
    model.num_labels=$NUM_LABELS \
    optimization.lr=[1e-06] \
    optimization.max_epoch=140 \
    dataset.batch_size=256 \
    dataset.num_workers=5 \
    dataset.disable_validation=true \
    distributed_training.distributed_world_size=1 \
    distributed_training.find_unused_parameters=True \
    checkpoint.save_dir=$OUTPUT_DIR \
    checkpoint.save_interval=1 \
    checkpoint.keep_last_epochs=0 \
    common.log_format=csv \
    +task.label_file=$LABEL_DIR/y.npy \
    +criterion.pos_weight=$POS_WEIGHT \
    --config-dir $FAIRSEQ_SIGNALS_ROOT/examples/w2v_cmsc/config/finetuning/ecg_transformer \
    --config-name diagnosis
  ```

*Notes:*
- With CMSC pretraining, the batch size refers to pairs of adjacent segments. Therefore, the effective pretraining batch size is `64 pairs * 2 segments per pair * 4 GPUs * 2 gradient accumulations (update_freq) = 1024 segments`.
- ECG-FM has 311,940,352 parameters, whereas the base model has 90,883,072 parameters. We would not suggest pretraining a large model having only those public data sources (PhysioNet 2021 and MIMIC-IV-ECG) used in the paper.

### Labeling Functionality
Functionality for our comphensive free-text pattern matching and knowledge graph based label manipulation will be made available soon!

## Questions
Inquiries may be directed to kaden.mckeen@mail.utoronto.ca.
