Foundation model of ECG diagnosis: Diagnostics and explanations of any form and rhythm on ECG

Abstract

We propose a knowledge-enhanced electrocardiogram (ECG) diagnosis foundation model (KED) that utilizes large language models to incorporate domain-specific knowledge of ECG signals. This model is trained on 800,000 ECGs from nearly 160,000 unique patients. Despite being trained on single-center data, KED demonstrates exceptional zero-shot diagnosis performance across various regions, including different locales in China, the United States, and other regions. This performance spans across all age groups for various conditions such as morphological abnormalities, rhythm abnormalities, conduction blocks, hypertrophy, myocardial ischemia, and infarction. Moreover, KED exhibits robust performance on diseases it has not encountered during its training. When compared to three experienced cardiologists on real clinical datasets, the model achieves comparable performance in zero-shot diagnosis of seven common clinical ECG types. We concentrate on the zero-shot diagnostic capability and the generalization performance of the proposed ECG foundation model, particularly in the context of external multi-center data and previously unseen disease.

Keywords: cardiovascular diseases; contrast learning; electrocardiograms; foundation model; knowledge enhancement; multimodal; signal-language model; zero-shot diagnosis.