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 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.
Clone fairseq_signals and refer to the requirements and installation section in the top-level README.
Please refer to our inference quickstart tutorial, which outlines inference and visualization pipelines.
Model checkpoints have been made publicly available for download on HuggingFace. Specifically, there is:
mimic_iv_ecg_physionet_pretrained.pt
- Pretrained on MIMIC-IV-ECG v1.0 and PhysioNet 2021 v1.0.3.
mimic_iv_ecg_finetuned.pt
- Finetuned from
mimic_iv_ecg_physionet_pretrained.pton MIMIC-IV-ECG v1.0 dataset.
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.
We implemented a flexible, end-to-end, multi-source data preprocessing pipeline. Please refer to it here.
The command-line inference tutorial 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 framework. To maximize reproducibility, we have provided configuration files.
Our pretraining uses the mimic_iv_ecg_physionet_pretrained.yaml config (can modify 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 $FAIRSEQ_SIGNALS_ROOT/examples/w2v_cmsc/config/pretraining \
--config-name w2v_cmsc_rlm
Notes:
- With CMSC pretraining, the batch size refers to pairs of adjacent segments. Therefore, the effective pretraining batch size is
64 pairs * 2 segments per pair * 4 GPUs * 2 gradient accumulations (update_freq) = 1024 segments.
Our finetuning uses the mimic_iv_ecg_finetuned.yaml config (can modify diagnosis.yaml as desired).
This command highlights some popular config overrides:
FAIRSEQ_SIGNALS_ROOT="<TODO>"
PRETRAINED_MODEL="<TODO>"
MANIFEST_DIR="<TODO>"
LABEL_DIR="<TODO>"
OUTPUT_DIR="<TODO>"
NUM_LABELS=$(($(wc -l < "$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
Functionality for our comphensive free-text pattern matching and knowledge graph-based label manipulation is showcased in the labeler.ipynb notebook.
Inquiries may be directed to [email protected].