Electrocardiograms (ECGs) are commonly used by cardiologists to detect
heart-related pathological conditions. Reliable collections of ECGs are crucial
for precise diagnosis. However, in clinical practice, the assignment of
captured ECG recordings to incorrect patients can occur inadvertently. In
collaboration with a clinical and research facility which recognized this
challenge and reached out to us, we present a study that addresses this issue.
In this work, we propose a small and efficient neural-network based model for
determining whether two ECGs originate from the same patient. Our model
demonstrates great generalization capabilities and achieves state-of-the-art
performance in gallery-probe patient identification on PTB-XL while utilizing
760x fewer parameters. Furthermore, we present a technique leveraging our model
for detection of recording-assignment mistakes, showcasing its applicability in
a realistic scenario. Finally, we evaluate our model on a newly collected ECG
dataset specifically curated for this study, and make it public for the
research community.

本研究旨在解决电信号记录错误的问题，通过提出一种小型且高效的基于神经网络的模型用于确定两个心电监护图是否来自同一患者，其在不到平均参数数量 760 倍的情况下表现出极好的推理能力，并将其在新收集的心电图数据集上进行了评估，并向研究社区公开发布。