An Electrocardiogram Multi-task Benchmark with Comprehensive Evaluations and Insightful Findings
Yuhao Xu, Jiaying Lu, Sirui Ding, Defu Cao, Xiao Hu, Carl Yang

TL;DR
This paper evaluates the effectiveness of foundation models in ECG analysis, demonstrating their competitive performance and providing comprehensive insights into their capabilities and limitations for healthcare applications.
Contribution
It introduces a multi-task ECG benchmark and systematically compares foundation models with traditional deep learning models, offering new insights into their utility in physiological waveform analysis.
Findings
Foundation models achieve up to 80% accuracy in ECG tasks
Comprehensive analysis of model strengths and limitations
Publicly available dataset and code for further research
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 non-invasive 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, we evaluate language/general time-series/ECG foundation models in comparison with time-series deep learning models. The experimental results show…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsECG Monitoring and Analysis · Non-Invasive Vital Sign Monitoring · Heart Rate Variability and Autonomic Control
