# Unsupervised Pre-Training Using Masked Autoencoders for ECG Analysis

arXiv: https://arxiv.org/abs/2310.11153 · HTML: https://mlsanity.com/paper/2310.11153/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 17 Oct 2023.

## Builds on (most cited first, 4 of 4)

- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://mlsanity.com/paper/2301.00808/) (2023)
- [Continuous User Authentication using IoT Wearable Sensors](https://mlsanity.com/paper/2105.05126/) (2021)

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

- [Learning General Representation of 12-Lead Electrocardiogram with a Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2410.08559/) (2024)
- [Global and Local Contrastive Learning for Joint Representations from Cardiac MRI and ECG](https://mlsanity.com/paper/2506.20683/) (2025)
- [HeartBERT: A Self-Supervised ECG Embedding Model for Efficient and Effective Medical Signal Analysis](https://mlsanity.com/paper/2411.11896/) (2024)
- [Modally Reduced Representation Learning of Multi-Lead ECG Signals through Simultaneous Alignment and Reconstruction](https://mlsanity.com/paper/2405.19359/) (2024)
- [Masked Autoencoders that Feel the Heart: Unveiling Simplicity Bias for ECG Analyses](https://mlsanity.com/paper/2506.22495/) (2025)
- [CLARAE: Clarity Preserving Reconstruction AutoEncoder for Denoising and Rhythm Classification of Intracardiac Electrograms](https://mlsanity.com/paper/2510.17821/) (2025)
- [Neonatal Hypoxic-ischaemic Encephalopathy Classification from the EEG and HRV Signals Using a Conformer based Masked Autoencoder](https://mlsanity.com/paper/2607.23554/) (2026)

## Abstract (arXiv)

Unsupervised learning methods have become increasingly important in deep learning due to their demonstrated large utilization of datasets and higher accuracy in computer vision and natural language processing tasks. There is a growing trend to extend unsupervised learning methods to other domains, which helps to utilize a large amount of unlabelled data. This paper proposes an unsupervised pre-training technique based on masked autoencoder (MAE) for electrocardiogram (ECG) signals. In addition, we propose a task-specific fine-tuning to form a complete framework for ECG analysis. The framework is high-level, universal, and not individually adapted to specific model architectures or tasks. Experiments are conducted using various model architectures and large-scale datasets, resulting in an accuracy of 94.39% on the MITDB dataset for ECG arrhythmia classification task. The result shows a better performance for the classification of previously unseen data for the proposed approach compared to fully supervised methods.