论文标题
心电图中发现的隐藏波：一种声音自动解释方法
The hidden waves in the ECG uncovered: a sound automated interpretation method
论文作者
论文摘要
本文提出了一种分析心律数据的新方法。心跳分解为五个基本$ p $，$ q $，$ r $，$ s $和$ t $ waves以及一个错误术语，以说明数据中的人工制品，该数据提供了对心脏电气系统的有意义的物理解释。每个波的形态使用四个允许对心跳中所有不同模式的参数进行简洁描述，从而分化 这种多功能方法解决了诸如可解释特征的提取，基本波的基准标记的检测或合成数据的产生以及信号的降解等问题。然而，与刚性，脆弱和黑匣子机器学习程序相比，这一新发现最大的好处是心脏异常以及其他临床用途的自动诊断，并具有很大的优势。 本文在实践中显示了该方法的巨大潜力。具体而言，使用模拟和真实数据对歧视主体，表征形态和检测基准标记（参考点）的能力进行了数值验证，从而证明了它的表现优于其竞争对手。
A novel approach for analysing cardiac rhythm data is presented in this paper. Heartbeats are decomposed into the five fundamental $P$, $Q$, $R$, $S$ and $T$ waves plus an error term to account for artefacts in the data which provides a meaningful, physical interpretation of the heart's electric system. The morphology of each wave is concisely described using four parameters that allow to all the different patterns in heartbeats be characterized and thus differentiated This multi-purpose approach solves such questions as the extraction of interpretable features, the detection of the fiducial marks of the fundamental waves, or the generation of synthetic data and the denoising of signals. Yet, the greatest benefit from this new discovery will be the automatic diagnosis of heart anomalies as well as other clinical uses with great advantages compared to the rigid, vulnerable and black box machine learning procedures, widely used in medical devices. The paper shows the enormous potential of the method in practice; specifically, the capability to discriminate subjects, characterize morphologies and detect the fiducial marks (reference points) are validated numerically using simulated and real data, thus proving that it outperforms its competitors.
