ECGrecover：一种用于心电图信号补全的深度学习方法
ECGrecover: a Deep Learning Approach for Electrocardiogram Signal Completion
AI总结：
提出ECGrecover，一种基于U-Net和复合目标函数的深度学习方法，用于从缺失或不完整信号中重建完整12导联心电图，并在真实数据集上优于现有方法。
摘要：
在这项工作中，我们解决了从不完整部分重建完整12导联心电图信号的挑战。我们关注两个主要场景：（i）重建心电图导联内缺失的信号段，以及（ii）从另一个独特导联的信号中恢复整个导联。两个新兴的临床应用强调了我们工作的相关性。第一个是日益增长的将纸质存储的心电图数字化以用于基于人工智能的应用的需求，这些应用通常仅限于数字12导联10秒心电图。第二个是可穿戴设备的广泛使用，这些设备记录心电图但通常仅捕获一个或几个导联。在这两种情况下，不可忽略的信息量会丢失或未被记录。我们的方法旨在恢复这些缺失的信号。我们提出了ECGrecover，一种在新型复合目标函数上训练的U-Net神经网络模型，以解决重建问题。该函数通过结合重建信号与真实数字信号之间的幅度距离和随时间同步性，整合了心电图的空间和时间特征。我们使用了真实的心电图数据集，并通过全面评估将ECGrecover与三种基于生成对抗网络（EKGAN、Pix2Pix）的最先进方法以及CopyPaste策略进行了比较。结果表明，ECGrecover在标准失真度量以及保留关键心电图特征（特别是P波、QRS波群和T波坐标）方面始终优于最先进的方法。
英文摘要：
In this work, we address the challenge of reconstructing the complete 12-lead ECG signal from its incomplete parts. We focus on two main scenarios: (i) reconstructing missing signal segments within an ECG lead and (ii) recovering entire leads from signal in another unique lead. Two emerging clinical applications emphasize the relevance of our work. The first is the increasing need to digitize paper-stored ECGs for utilization in AI-based applications, often limited to digital 12 lead 10s ECGs. The second is the widespread use of wearable devices that record ECGs but typically capture only one or a few leads. In both cases, a non-negligible amount of information is lost or not recorded. Our approach aims to recover this missing signal. We propose ECGrecover, a U-Net neural network model trained on a novel composite objective function to address the reconstruction problem. This function incorporates both spatial and temporal features of the ECG by combining the distance in amplitude and sycnhronization through time between the reconstructed and the real digital signals. We used real-life ECG datasets and through comprehensive assessments compared ECGrecover with three state-of-the-art methods based on generative adversarial networks (EKGAN, Pix2Pix) as well as the CopyPaste strategy. The results demonstrated that ECGrecover consistently outperformed state-of-the-art methods in standard distortion metrics as well as in preserving critical ECG characteristics, particularly the P, QRS, and T wave coordinates.
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。
