UNASSIGNED: This article addresses this challenge by introducing two key innovations. First, we develop a synthetic data generation scheme that probabilistically constructs unseen ECG traces from \"pools\" of fundamental segments extracted from existing databases. A set of rules guides the arrangement of these segments into coherent synthetic traces, while expert domain knowledge ensures the realism of the generated traces, increasing the input variability for training the model. Second, we propose two novel segmentation-based loss functions that encourage the accurate prediction of the number of independent ECG structures and promote tighter segmentation boundaries by focusing on a reduced number of samples.
UNASSIGNED: The proposed approach achieves remarkable performance, with a F 1 -score of 99.38% and delineation errors of 2.19 ± 17.73 ms and 4.45 ± 18.32 ms for ECG segment onsets and offsets across the P, QRS, and T waves. These results, aggregated from three diverse freely available databases (QT, LU, and Zhejiang), surpass current state-of-the-art detection and delineation approaches.
UNASSIGNED: Notably, the model demonstrated exceptional performance despite variations in lead configurations, sampling frequencies, and represented pathophysiology mechanisms, underscoring its robust generalisation capabilities. Real-world examples, featuring clinical data with various pathologies, illustrate the potential of our approach to streamline ECG analysis across different medical settings, fostered by releasing the codes as open source.
■本文通过介绍两个关键创新来解决这一挑战。首先,我们开发了一种合成数据生成方案,该方案从现有数据库中提取的基本片段的\"池\"中概率地构建看不见的ECG迹线。一组规则将这些片段的排列引导成连贯的合成痕迹,而专家领域知识确保生成的痕迹的真实性,增加训练模型的输入变异性。第二,我们提出了两个新颖的基于分割的损失函数,它们鼓励准确预测独立ECG结构的数量,并通过关注减少的样本数来促进更紧密的分割边界.
■所提出的方法实现了卓越的性能,F1分数为99.38%,心电图节段的起始和偏移在P上的描绘误差为2.19±17.73ms和4.45±18.32ms,QRS,T波。这些结果,从三个不同的免费数据库(QT,LU,和浙江),超越了当前最先进的检测和描绘方法。
■值得注意的是,该模型表现出卓越的性能,尽管在引线配置的变化,采样频率,并代表病理生理机制,强调其强大的泛化能力。现实世界的例子,具有各种病理的临床数据,说明了我们在不同医疗环境中简化ECG分析的方法的潜力,通过释放代码作为开源来促进。