# ECG Language Processing (ELP): a New Technique to Analyze ECG Signals

arXiv: https://arxiv.org/abs/2006.08841 · HTML: https://mlsanity.com/paper/2006.08841/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2020.

## 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)
- [Hierarchical Deep Learning with Generative Adversarial Network for Automatic Cardiac Diagnosis from ECG Signals](https://mlsanity.com/paper/2210.11408/) (2022)
- [ECGBERT: Understanding Hidden Language of ECGs with Self-Supervised Representation Learning](https://mlsanity.com/paper/2306.06340/) (2023)
- [HeartBERT: A Self-Supervised ECG Embedding Model for Efficient and Effective Medical Signal Analysis](https://mlsanity.com/paper/2411.11896/) (2024)
- [RhythmBERT: A Self-Supervised Language Model Based on Latent Representations of ECG Waveforms for Heart Disease Detection](https://mlsanity.com/paper/2602.23060/) (2026)
- [Enhancing Electrocardiogram Signal Analysis Using NLP-Inspired Techniques: A Novel Approach with Embedding and Self-Attention](https://mlsanity.com/paper/2407.11102/) (2024)
- [BEAT-Net: Injecting Biomimetic Spatio-Temporal Priors for Interpretable ECG Diagnosis](https://mlsanity.com/paper/2601.07316/) (2026)

## Abstract (arXiv)

A language is constructed of a finite/infinite set of sentences composing of words. Similar to natural languages, Electrocardiogram (ECG) signal, the most common noninvasive tool to study the functionality of the heart and diagnose several abnormal arrhythmias, is made up of sequences of three or four distinct waves including the P-wave, QRS complex, T-wave and U-wave. An ECG signal may contain several different varieties of each wave (e.g., the QRS complex can have various appearances). For this reason, the ECG signal is a sequence of heartbeats similar to sentences in natural languages) and each heartbeat is composed of a set of waves (similar to words in a sentence) of different morphologies. Analogous to natural language processing (NLP) which is used to help computers understand and interpret the human's natural language, it is possible to develop methods inspired by NLP to aid computers to gain a deeper understanding of Electrocardiogram signals. In this work, our goal is to propose a novel ECG analysis technique, textitECG language processing (ELP), focusing on empowering computers to understand ECG signals in a way physicians do. We evaluated the proposed method on two tasks including the classification of heartbeats and the detection of atrial fibrillation in the ECG signals. Experimental results on three databases (i.e., PhysionNet's MIT-BIH, MIT-BIH AFIB and PhysioNet Challenge 2017 AFIB Dataset databases) reveal that the proposed method is a general idea that can be applied to a variety of biomedical applications and is able to achieve remarkable performance.