ElectroCardioGuard：通过神经网络防止心电图数据库中的患者身份误配
ElectroCardioGuard: Preventing Patient Misidentification in Electrocardiogram Databases through Neural Networks
AI总结：
本文提出小型高效神经网络模型ElectroCardioGuard，通过判断两份心电图是否来自同一患者，以760倍更少参数在PTB-XL上取得最先进患者识别性能，并用于检测记录分配错误。
摘要：
心电图（ECG）是心脏病专家常用于检测心脏相关病理状况的工具。可靠的心电图集合对于精确诊断至关重要。然而，在临床实践中，将采集到的心电图记录错误分配给患者的情况可能无意中发生。我们与一家认识到这一挑战并主动联系我们的临床与研究机构合作，提出了一项针对该问题的研究。在这项工作中，我们提出了一种小型且高效的基于神经网络的模型，用于判断两份心电图是否来自同一患者。我们的模型展现出很强的泛化能力，并在PTB-XL上的图库-探针患者识别任务中取得了最先进的性能，同时使用的参数量减少了760倍。此外，我们提出了一种利用该模型检测记录分配错误的技术，展示了其在现实场景中的适用性。最后，我们在一个专门为本研究新采集的心电图数据集上评估了我们的模型，并将其公开供研究社区使用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
