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A Simple Framework for Contrastive Learning of Visual Representations

We provide various perturbation methods with ECG data for SimCLR model, which are listed as:

  • Random lead masking
  • Powerline noise
  • EMG noise
  • Baseline shift
  • Baseline wander

Before training the model, please follow these instructions to install fairseq-signals and prepare required datasets.

Pre-training a new ECG Transformer model

$ fairseq-hydra-train \
    task.data=/path/to/manifest/pretrain \
    --config-dir examples/simclr/config/pretraining/ecg_transformer \
    --config-name simclr_rlm

If you want to apply more augmentations, refer to examples/simclr/config/pretraining/.../simclr_augs.yaml.

Fine-tuning a pre-trained ECG Transformer model

Fine-tune on the Cardiac Arrhythmia Classification task

$ fairseq-hydra-train \
    task.data=/path/to/manifest/finetune \
    model.model_path=/path/to/checkpoint.pt \
    --config-dir examples/simclr/config/finetuning/ecg_transformer \
    --config-name diagnosis

If you want to use CinC score as an evaluation metric, add command line parameters (before --config-dir) criterion.report_cinc_score=True criterion.weights_file=/path/to/weights.csv

Note that you can download weights.csv file from here.

Fine-tune on the Patient Identification task

$ fairseq-hydra-train \
    task.data=/path/to/manifest/identify \
    model.model_path=/path/to/checkpoint.pt \
    model.num_labels=$N \
    --config-dir examples/simclr/config/finetuning/ecg_transformer \
    --config-name identification

$N should be set to the number of unique patients in the training dataset. You can manually open /path/to/manifest/identify/train.tsv file and check the last line of that file. For example, if the last line is like *.mat 2500 69977, then $N should be set to 69978.

Note that if you want to train with PhysioNet2021 dataset and test with PTB-XL dataset, prepare data manifest for PhysioNet2021 with $valid=0 and PTB-XL with $valid=1.0 seperately and place them to the same manifest directory like this:

path/to/manifest/identify
├─ train.tsv
├─ valid_gallery.tsv
└─ valid_probe.tsv

Note: valid_*.tsv should have been from PTB-XL dataset while train.tsv should have been from PhysioNet2021 dataset.