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

Jiarui Jin, Haoyu Wang, Jun Li, Sichao Huang, Jiahui Pan, Shenda Hong

Keywords: Electrocardiogram (ECG), ECG language processing, self-supervised learning, transformer.

TL;DR: Heartbeats as Words, A Novel Self-supervised Learning ECG Framework for ECG Language Processing

Abstract: Electrocardiograms (ECGs) are essential for the clinical diagnosis of arrhythmias and other heart diseases, but deep learning methods based on ECGs often face limitations due to the need for high-quality annotations. Although previous ECG self-supervised learning (eSSL) methods have made significant progress, they typically treat ECG signals as general time-series data, using fixed steps and window sizes, which often ignore the heartbeat and rhythmic characteristics and potential 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 propose HeartLang, a novel self-supervised learning framework for ECG language processing. Within this framework, we construct an ECG vocabulary and pre-train the model using masked prediction on ECG sentences to learn both heartbeat-level and rhythm-level representations, uncovering the latent semantic relationships in ECG signals. We also developed three parameter scales for HeartLang, namely, HeartLang-Small, HeartLang-Base, and HeartLang-Large, and conducted pre-training and downstream task testing on the standard benchmark dataset PTB-XL.The experimental results demonstrate that our method exhibits superior performance compared to other eSSL methods.

Submission Number: 36