# Analysis of Augmentations for Contrastive ECG Representation Learning

arXiv: https://arxiv.org/abs/2206.07656 · HTML: https://mlsanity.com/paper/2206.07656/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 57 references that are in the map.
- First posted on arXiv 30 May 2022.

## Builds on (most cited first, 21 of 21)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [Colorful Image Colorization](https://mlsanity.com/paper/1603.08511/) (2016)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [What Makes for Good Views for Contrastive Learning?](https://mlsanity.com/paper/2005.10243/) (2020)
- [Time-Series Representation Learning via Temporal and Contextual Contrasting](https://mlsanity.com/paper/2106.14112/) (2021)
- [CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients](https://mlsanity.com/paper/2005.13249/) (2020)
- [Contrastive Learning with Stronger Augmentations](https://mlsanity.com/paper/2104.07713/) (2021)
- [Self-supervised ECG Representation Learning for Emotion Recognition](https://mlsanity.com/paper/2002.03898/) (2020)
- [ECG Heartbeat Classification: A Deep Transferable Representation](https://mlsanity.com/paper/1805.00794/) (2018)
- [Subject-Aware Contrastive Learning for Biosignals](https://mlsanity.com/paper/2007.04871/) (2020)
- [Transitive Invariance for Self-supervised Visual Representation Learning](https://mlsanity.com/paper/1708.02901/) (2017)
- [3KG: Contrastive Learning of 12-Lead Electrocardiograms using Physiologically-Inspired Augmentations](https://mlsanity.com/paper/2106.04452/) (2021)
- [Self-supervised Learning for ECG-based Emotion Recognition](https://mlsanity.com/paper/1910.07497/) (2019)
- [ECG arrhythmia classification using a 2-D convolutional neural network](https://mlsanity.com/paper/1804.06812/) (2018)
- [Detection of Maternal and Fetal Stress from the Electrocardiogram with Self-Supervised Representation Learning](https://mlsanity.com/paper/2011.02000/) (2020)

## Built on by (most cited first, 12 of 12)

- [Guiding Masked Representation Learning to Capture Spatio-Temporal Relationship of Electrocardiogram](https://mlsanity.com/paper/2402.09450/) (2024)
- [Self-Supervised Pre-Training with Joint-Embedding Predictive Architecture Boosts ECG Classification Performance](https://mlsanity.com/paper/2410.13867/) (2024)
- [In-Distribution and Out-of-Distribution Self-supervised ECG Representation Learning for Arrhythmia Detection](https://mlsanity.com/paper/2304.06427/) (2023)
- [CLEF: Clinically-Guided Contrastive Learning for Electrocardiogram Foundation Models](https://mlsanity.com/paper/2512.02180/) (2025)
- [Dynamic Prototype Rehearsal for Continual ECG Arrhythmia Detection](https://mlsanity.com/paper/2501.07555/) (2025)
- [Self-DANA: A Resource-Efficient Channel-Adaptive Self-Supervised Approach for ECG Foundation Models](https://mlsanity.com/paper/2507.14151/) (2025)
- [Remote Heart Rate Monitoring in Smart Environments from Videos with Self-supervised Pre-training](https://mlsanity.com/paper/2310.15388/) (2023)
- [Online Multi-level Contrastive Representation Distillation for Cross-Subject fNIRS Emotion Recognition](https://mlsanity.com/paper/2409.16081/) (2024)
- [Uncertainty-Aware Multi-view Arrhythmia Classification from ECG](https://mlsanity.com/paper/2506.06342/) (2025)
- [ECG-Soup: Harnessing Multi-Layer Synergy for ECG Foundation Models](https://mlsanity.com/paper/2509.00102/) (2025)
- [Self-Supervised and Topological Signal-Quality Assessment for Any PPG Device](https://mlsanity.com/paper/2509.12510/) (2025)
- [Information-theoretic Multimodal Representation Learning for Electrocardiogram Signals](https://mlsanity.com/paper/2605.27583/) (2026)

## Abstract (arXiv)

This paper systematically investigates the effectiveness of various augmentations for contrastive self-supervised learning of electrocardiogram (ECG) signals and identifies the best parameters. The baseline of our proposed self-supervised framework consists of two main parts: the contrastive learning and the downstream task. In the first stage, we train an encoder using a number of augmentations to extract generalizable ECG signal representations. We then freeze the encoder and finetune a few linear layers with different amounts of labelled data for downstream arrhythmia detection. We then experiment with various augmentations techniques and explore a range of parameters. Our experiments are done on PTB-XL, a large and publicly available 12-lead ECG dataset. The results show that applying augmentations in a specific range of complexities works better for self-supervised contrastive learning. For instance, when adding Gaussian noise, a sigma in the range of 0.1 to 0.2 achieves better results, while poor training occurs when the added noise is too small or too large (outside of the specified range). A similar trend is observed with other augmentations, demonstrating the importance of selecting the optimum level of difficulty for the added augmentations, as augmentations that are too simple will not result in effective training, while augmentations that are too difficult will also prevent the model from effective learning of generalized representations. Our work can influence future research on self-supervised contrastive learning on bio-signals and aid in selecting optimum parameters for different augmentations.