Department of Statistics and Operations Research, Universidad de Valladolid, Paseo de Belén 7, Valladolid 47011, Spain.
Department of Statistics and Operations Research, Universidad de Valladolid, Paseo de Belén 7, Valladolid 47011, Spain.
Comput Methods Programs Biomed. 2022 Jun;221:106807. doi: 10.1016/j.cmpb.2022.106807. Epub 2022 Apr 22.
The automatic diagnosis of heart diseases from the electrocardiogram (ECG) signal is crucial in clinical decision-making. However, the use of computer-based decision rules in clinical practice is still deficient, mainly due to their complexity and a lack of medical interpretation. The objetive of this research is to address these issues by providing valuable diagnostic rules that can be easily implemented in clinical practice. In this research, efficient diagnostic rules friendly in clinical practice are provided.
In this paper, interesting parameters obtained from the ECG signals analysis are presented and two simple rules for automatic diagnosis of Bundle Branch Blocks are defined using new markers derived from the so-called FMM delineator. The main advantages of these markers are the good statistical properties and their clear interpretation in clinically meaningful terms.
High sensitivity and specificity values have been obtained using the proposed rules with data from more than 35,000 patients from well known benchmarking databases. In particular, to identify Complete Left Bundle Branch Blocks and differentiate this condition from subjects without heart diseases, sensitivity and specificity values ranging from 93% to 99% and from 96% to 99%, respectively. The new markers and the automatic diagnosis are easily available at https://fmmmodel.shinyapps.io/fmmEcg/, an app specifically developed for any given ECG signal.
The proposal is different from others in the literature and it is compelling for three main reasons. On the one hand, the markers have a concise electrocardiographic interpretation. On the other hand, the diagnosis rules have a very high accuracy. Finally, the markers can be provided by any device that registers the ECG signal and the automatic diagnosis is made straightforwardly, in contrast to the black-box and deep learning algorithms.
从心电图（ECG）信号中自动诊断心脏病在临床决策中至关重要。然而，计算机决策规则在临床实践中的应用仍然不足，主要是由于其复杂性和缺乏医学解释。本研究的目的是通过提供易于在临床实践中实施的有价值的诊断规则来解决这些问题。在本研究中，提供了在临床实践中友好的高效诊断规则。
在本文中，提出了从 ECG 信号分析中获得的有趣参数，并使用源自所谓的 FMM 描绘符的新标记物定义了用于自动诊断束支传导阻滞的两个简单规则。这些标记物的主要优点是具有良好的统计特性，并且可以用临床上有意义的术语进行清晰解释。
使用从著名基准数据库中的 35000 多个患者获得的数据，提出的规则获得了高灵敏度和特异性值。特别是，为了识别完全性左束支传导阻滞并将这种情况与无心脏病的患者区分开来，灵敏度和特异性值分别为 93%至 99%和 96%至 99%。新标记物和自动诊断可在 https://fmmmodel.shinyapps.io/fmmEcg/ 上轻松获得，这是一个专门为任何给定 ECG 信号开发的应用程序。
该提案与文献中的其他提案不同，主要有三个原因。一方面，标记物具有简洁的心电图解释。另一方面，诊断规则具有非常高的准确性。最后，任何记录 ECG 信号的设备都可以提供标记物，并且可以直接进行自动诊断，与黑盒和深度学习算法形成对比。
