Papers › Data Augmentation for Electrocardiograms

Data Augmentation for Electrocardiograms

9 Apr 2022arXiv:2204.04360archive 2025-07-28

Aniruddh Raghu, Divya Shanmugam, Eugene Pomerantsev, John Guttag, Collin M. Stultz

Neural network models have demonstrated impressive performance in predicting pathologies and outcomes from the 12-lead electrocardiogram (ECG). However, these models often need to be trained with large, labelled datasets, which are not available for many predictive tasks of interest. In this work, we perform an empirical study examining whether training time data augmentation methods can be used to improve performance on such data-scarce ECG prediction problems. We investigate how data augmentation strategies impact model performance when detecting cardiac abnormalities from the ECG. Motivated by our finding that the effectiveness of existing augmentation strategies is highly task-dependent, we introduce a new method, TaskAug, which defines a flexible augmentation policy that is optimized on a per-task basis. We outline an efficient learning algorithm to do so that leverages recent work in nested optimization and implicit differentiation. In experiments, considering three datasets and eight predictive tasks, we find that TaskAug is competitive with or improves on prior work, and the learned policies shed light on what transformations are most effective for different tasks. We distill key insights from our experimental evaluation, generating a set of best practices for applying data augmentation to ECG prediction problems.

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all_ops aniruddhraghu/ecg_aug/aug_policy.py official repository unverified Apache-2.0 (permissive) · f77132e6e07362a5 · report
baseline_wander aniruddhraghu/ecg_aug/functional.py official repository unverified Apache-2.0 (permissive) · 3c3fcd9021d77049 · report
conv15x1 aniruddhraghu/ecg_aug/models.py official repository unverified Apache-2.0 (permissive) · babbdd13569d12fb · report
full_policy aniruddhraghu/ecg_aug/aug_policy.py official repository unverified Apache-2.0 (permissive) · 5745974ee9419ea2 · report
gather_flat_grad aniruddhraghu/ecg_aug/taskaug.py official repository unverified Apache-2.0 (permissive) · 39ed79585e665e7c · report
gaussian_noise aniruddhraghu/ecg_aug/functional.py official repository unverified Apache-2.0 (permissive) · 16242b2c7a63e743 · report
get_hyper_train_flat aniruddhraghu/ecg_aug/taskaug.py official repository unverified Apache-2.0 (permissive) · 46f2a67ab0375bf0 · report
get_loss aniruddhraghu/ecg_aug/baseline.py official repository unverified Apache-2.0 (permissive) · c8cc5d297ead6d57 · report
get_preds aniruddhraghu/ecg_aug/baseline.py official repository unverified Apache-2.0 (permissive) · 34615160a839a22d · report
neumann_hyperstep_preconditioner aniruddhraghu/ecg_aug/taskaug.py official repository unverified Apache-2.0 (permissive) · db9e01c78411b855 · report
rand_temporal_warp aniruddhraghu/ecg_aug/functional.py official repository unverified Apache-2.0 (permissive) · cde3852e96a5a541 · report
resnet18 aniruddhraghu/ecg_aug/models.py official repository unverified Apache-2.0 (permissive) · 81f0ae2595a80555 · report
resnet34 aniruddhraghu/ecg_aug/models.py official repository unverified Apache-2.0 (permissive) · d4af8e9568a847d3 · report
update_lossdict aniruddhraghu/ecg_aug/baseline.py official repository unverified Apache-2.0 (permissive) · 02aeb8cc5d89ffe6 · report

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