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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.
## 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.
### 🚀 Quick Start
Please refer to our [inference quickstart tutorial](https://github.com/bowang-lab/ECG-FM/blob/main/notebooks/infer_quickstart.ipynb), which outlines inference and visualization pipelines.
### 📦 Model
Model checkpoints have been made publicly available for [download on HuggingFace](https://huggingface.co/wanglab/ecg-fm). Specifically, there is:
`mimic_iv_ecg_physionet_pretrained.pt`
- 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/).
`mimic_iv_ecg_finetuned.pt`
- Finetuned from `mimic_iv_ecg_physionet_pretrained.pt` on [MIMIC-IV-ECG v1.0 dataset](https://physionet.org/content/mimic-iv-ecg/1.0/).
ECG-FM has 90.9 million parameters, adopts the wav2vec 2.0 architecture, and was pretrained using the W2V+CMSC+RLM (WCR) method. Further details are available in our [paper](https://arxiv.org/abs/2408.05178).
### 🫀 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).
### ⚙️ Command-line Usage
The [command-line inference tutorial](https://github.com/bowang-lab/ECG-FM/blob/main/notebooks/infer_cli.ipynb) describes the result extraction and post-processing. There is also a script for performing linear probing experiments.
All 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).
#### Pretraining
Our pretraining uses the `mimic_iv_ecg_physionet_pretrained.yaml` config (can modify [w2v_cmsc_rlm.yaml](https://github.com/Jwoo5/fairseq-signals/blob/master/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=""
MANIFEST_DIR="/cmsc"
OUTPUT_DIR=""
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 $FAIRSEQ_SIGNALS_ROOT/examples/w2v_cmsc/config/pretraining \
--config-name w2v_cmsc_rlm
```
*Notes:*
- With CMSC pretraining, the batch size refers to pairs of adjacent segments. Therefore, the effective pretraining batch size is `64 pairs * 2 segments per pair * 4 GPUs * 2 gradient accumulations (update_freq) = 1024 segments`.
#### Finetuning
Our finetuning uses the `mimic_iv_ecg_finetuned.yaml` config (can modify [diagnosis.yaml](https://github.com/Jwoo5/fairseq-signals/blob/master/examples/w2v_cmsc/config/finetuning/ecg_transformer/diagnosis.yaml) as desired).
This command highlights some popular config overrides:
```
FAIRSEQ_SIGNALS_ROOT=""
PRETRAINED_MODEL=""
MANIFEST_DIR=""
LABEL_DIR=""
OUTPUT_DIR=""
NUM_LABELS=$(($(wc -l < "$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
```
### 🏷️ Labeler
Functionality for our comphensive free-text pattern matching and knowledge graph-based label manipulation is showcased in the [labeler.ipynb](https://github.com/bowang-lab/ECG-FM/blob/main/notebooks/infer_quickstart.ipynb) notebook.
## 💬 Questions
Inquiries may be directed to kaden.mckeen@mail.utoronto.ca.
