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An Electrocardiogram Foundation Model Built on over 10 Million Recordings with External Evaluation across Multiple Domains by Li, Jun; Aguirre, Aaron; Moura, Junior; Liu, Che; Zhong, Lanhai; Sun, Chenxi; Clifford, Gari; Westover, Brandon; Hong, Shenda is a scholarly article available to read on EtoBox.

What is An Electrocardiogram Foundation Model Built on over 10 Million Recordings with External Evaluation across Multiple Domains about?

Artificial intelligence (AI) has demonstrated significant potential in ECG analysis and cardiovascular disease assessment. Recently, foundation models have played a remarkable role in advancing medical AI. The development of an ECG foundation model holds the promise of elevating AI-ECG research to new heights. However, building such a model faces several challenges, including insufficient database sample sizes and inadequate generalization across multiple domains. Additionally, there is a notable performance gap between single-lead and multi-lead ECG analyses. We introduced an ECG Foundation Model (ECGFounder), a general-purpose model that leverages real-world ECG annotations from cardiology experts to broaden the diagnostic capabilities of ECG analysis. ECGFounder was trained on over 10 million ECGs with 150 label categories from the Harvard-Emory ECG Database, enabling comprehensive cardiovascular disease diagnosis through ECG analysis. The model is designed to be both an effective out-of-the-box solution, and a to be fine-tunable for downstream tasks, maximizing usability. Importantly, we extended its application to lower rank ECGs, and arbitrary single-lead ECGs in particular.

Author

Li, Jun; Aguirre, Aaron; Moura, Junior; Liu, Che; Zhong, Lanhai; Sun, Chenxi; Clifford, Gari; Westover, Brandon; Hong, Shenda

Published

2024

Language

EN