# A Simple Self-Supervised ECG Representation Learning Method via Manipulated Temporal-Spatial Reverse Detection

arXiv: https://arxiv.org/abs/2202.12458 · HTML: https://mlsanity.com/paper/2202.12458/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 25 Feb 2022.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Designing Network Design Spaces](https://mlsanity.com/paper/2003.13678/) (2020)
- [CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients](https://mlsanity.com/paper/2005.13249/) (2020)
- [HOLMES: Health OnLine Model Ensemble Serving for Deep Learning Models in Intensive Care Units](https://mlsanity.com/paper/2008.04063/) (2020)
- [Intra-Inter Subject Self-supervised Learning for Multivariate Cardiac Signals](https://mlsanity.com/paper/2109.08908/) (2021)
- [Self-supervised Learning for ECG-based Emotion Recognition](https://mlsanity.com/paper/1910.07497/) (2019)
- [Interpretable Deep Neural Networks for Single-Trial EEG Classification](https://mlsanity.com/paper/1604.08201/) (2016)

## Built on by (most cited first, 7 of 7)

- [Reading Your Heart: Learning ECG Words and Sentences via Pre-training ECG Language Model](https://mlsanity.com/paper/2502.10707/) (2025)
- [Personalized Anomaly Detection in PPG Data using Representation Learning and Biometric Identification](https://mlsanity.com/paper/2307.06380/) (2023)
- [Self-supervised inter-intra period-aware ECG representation learning for detecting atrial fibrillation](https://mlsanity.com/paper/2410.18094/) (2024)
- [FADE: Forecasting for Anomaly Detection on ECG](https://mlsanity.com/paper/2502.07389/) (2025)
- [ECG Latent Feature Extraction with Autoencoders for Downstream Prediction Tasks](https://mlsanity.com/paper/2508.00131/) (2025)
- [Position: Evaluation of ECG Representations Must Be Fixed](https://mlsanity.com/paper/2602.17531/) (2026)
- [CoRe-ECG: Advancing Self-Supervised Representation Learning for 12-Lead ECG via Contrastive and Reconstructive Synergy](https://mlsanity.com/paper/2604.11359/) (2026)

## Abstract (arXiv)

Learning representations from electrocardiogram (ECG) signals can serve as a fundamental step for different machine learning-based ECG tasks. In order to extract general ECG representations that can be adapted to various downstream tasks, the learning process needs to be based on a general ECG-related task which can be achieved through self-supervised learning (SSL). However, existing SSL approaches either fail to provide satisfactory ECG representations or require too much effort to construct the learning data. In this paper, we propose the T-S reverse detection, a simple yet effective self-supervised approach to learn ECG representations. Inspired by the temporal and spatial characteristics of ECG signals, we flip the original signals horizontally (temporal reverse), vertically (spatial reverse), and both horizontally and vertically (temporal-spatial reverse). Learning is then done by classifying four types of signals including the original one. To verify the effectiveness of the proposed method, we perform a downstream task to detect atrial fibrillation (AF) which is one of the most common ECG tasks. The results show that the ECG representations learned with our method achieve remarkable performance. Furthermore, after exploring the representation feature space and investigating salient ECG locations, we conclude that the temporal reverse is more effective for learning ECG representations than the spatial reverse.