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An Electrocardiogram Multi-task Benchmark with Comprehensive Evaluations and Insightful Findings

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📝 Original Info

  • Title: An Electrocardiogram Multi-task Benchmark with Comprehensive Evaluations and Insightful Findings
  • ArXiv ID: 2512.08954
  • Date: 2025-11-28
  • Authors: Yuhao Xu, Jiaying Lu, Sirui Ding, Defu Cao, Xiao Hu, Carl Yang

📝 Abstract

In the process of patient diagnosis, non-invasive measurements are widely used due to their low risks and quick results. Electrocardiogram (ECG), as a noninvasive method to collect heart activities, is used to diagnose cardiac conditions. Analyzing the ECG typically requires domain expertise, which is a roadblock to applying artificial intelligence (AI) for healthcare. Through advances in self-supervised learning and foundation models, AI systems can now acquire and leverage domain knowledge without relying solely on human expertise. However, there is a lack of comprehensive analyses over the foundation models' performance on ECG. This study aims to answer the research question: "Are Foundation Models Useful for ECG Analysis?" To address it...

📄 Full Content

Electrocardiogram (ECG) records the heart's electrical activities via skin-placed electrodes [1], producing waveforms that decipher cardiac functions. Its non-invasive nature and ease of collection make ECG ideal for continuous monitoring and early detection of cardiovascular abnormalities. ECG is used for diagnosing arrhythmias [2], myocardial infarctions [3] and analyzing heart rate variability [4], highlighting its diverse utilities. However, ECG analysis is challenging due to individual variations, complex waveforms, and susceptibility to noises [5]. Traditional ECG analysis

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