发表机构
Technical University of Denmark（丹麦技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于深度学习的信号质量评估模型，利用首个含物理与患者报告情境数据的动态心电图数据库训练，在多个数据库上表现稳定，并展示了结合情境数据研究复杂噪声的方法。
AI 中文摘要
本文提出并评估了一种基于深度学习的信号质量评估（SQA）模型，用于区分动态心电图（ECG）中的干净信号与噪声信号。该模型在哥本哈根健康技术中心-情境化心律失常数据库（CACHET-CADB）上进行训练，据我们所知，这是首个同时包含物理和患者报告情境数据的动态心电图数据库。模型在不同数据库（如MIT数据库和最新的PyhsioNet/Cinc Challenge 2021数据库）上表现出稳定的性能。随后，本文展示了如何利用SQA模型和物理情境数据来研究复杂的心电图噪声。
英文摘要
This paper presents and evaluates a Deep Learning-based (DL-based) Signal Quality Assessment (SQA) model to distinguish between clean and noisy ambulatory Electrocardiograms (ECG). The model is trained on Copenhagen Center for Health Technology-Contextualized Arrhythmia Database (CACHET-CADB), which, to the best of our knowledge, is the first ambulatory ECG database with both physical and patient-reported contextual data. The model shows stable performance on different databases such as MIT-databases and the latest PyhsioNet/Cinc Challenge 2021 databases. Subsequently, the paper demonstrates how complicated ECG noise can be investigated by the SQA model and the physical contextual data.
