Reading Your Heart: Learning ECG Words and Sentences via Pre-training ECG Language Model

TL;DR

This paper introduces HeartLang, a self-supervised ECG language model that treats heartbeats as words and rhythms as sentences, enabling improved representation learning for ECG analysis.

Contribution

The paper presents a novel ECG language perspective, a QRS-Tokenizer for semantic ECG sentence generation, and the largest heartbeat-based ECG vocabulary to enhance ECG understanding.

Findings

HeartLang outperforms existing eSSL methods on six ECG datasets.

The ECG vocabulary enables more meaningful semantic analysis.

The approach captures form and rhythm features effectively.

Abstract

Electrocardiogram (ECG) is essential for the clinical diagnosis of arrhythmias and other heart diseases, but deep learning methods based on ECG often face limitations due to the need for high-quality annotations. Although previous ECG self-supervised learning (eSSL) methods have made significant progress in representation learning from unannotated ECG data, they typically treat ECG signals as ordinary time-series data, segmenting the signals using fixed-size and fixed-step time windows, which often ignore the form and rhythm characteristics and latent semantic relationships in ECG signals. In this work, we introduce a novel perspective on ECG signals, treating heartbeats as words and rhythms as sentences. Based on this perspective, we first designed the QRS-Tokenizer, which generates semantically meaningful ECG sentences from the raw ECG signals. Building on these, we then propose HeartLang, a novel self-supervised learning framework for ECG language processing, learning general representations at form and rhythm levels. Additionally, we construct the largest heartbeat-based ECG vocabulary to date, which will further advance the development of ECG language processing. We evaluated HeartLang across six public ECG datasets, where it demonstrated robust competitiveness against other eSSL methods. Our data and code are publicly available at https://github.com/PKUDigitalHealth/HeartLang.