IEEE J Biomed Health Inform. 2018 Mar;22(2):409-420. doi: 10.1109/JBHI.2016.2631247. Epub 2016 Nov 21.
This paper aims to prove that automatic beat classification on ECG signals can be effectively solved with a pure knowledge-based approach, using an appropriate set of abstract features obtained from the interpretation of the physiological processes underlying the signal.
A set of qualitative morphological and rhythm features are obtained for each heartbeat as a result of the abductive interpretation of the ECG. Then, a QRS clustering algorithm is applied in order to reduce the effect of possible errors in the interpretation. Finally, a rule-based classifier assigns a tag to each cluster.
The method has been tested with the MIT-BIH Arrhythmia Database records, showing a significantly better performance than any other automatic approach in the state-of-the-art, and even improving most of the assisted approaches that require the intervention of an expert in the process.
The most relevant issues in ECG classification, related to a large extent to the variability of the signal patterns between different subjects and even in the same subject over time, will be overcome by changing the reasoning paradigm.
This paper demonstrates the power of an abductive framework for time-series interpretation to make a qualitative leap in the significance of the information extracted from the ECG by automatic methods.
本文旨在证明，通过使用从信号背后的生理过程解释中获得的适当的抽象特征集，基于纯知识的方法可以有效地解决心电图信号的自动节拍分类问题。
作为心电图推断的结果，为每个心跳获得了一组定性的形态和节律特征。然后，应用 QRS 聚类算法以减少解释中可能出现的错误的影响。最后，基于规则的分类器为每个聚类分配一个标签。
该方法已经在麻省理工学院生物医学工程系心律失常数据库记录上进行了测试，其性能明显优于现有技术中的任何其他自动方法，甚至改进了大多数需要专家干预的辅助方法。
通过改变推理范式，可以克服心电图分类中最相关的问题，这些问题在很大程度上与不同个体之间以及同一个体随时间变化的信号模式的可变性有关。
本文证明了溯因框架在时间序列解释方面的强大功能，通过自动方法从心电图中提取的信息的意义上实现了质的飞跃。
