IEEE J Biomed Health Inform. 2015 Sep;19(5):1660-71. doi: 10.1109/JBHI.2014.2361659. Epub 2014 Oct 8.
Continuous followup of heart condition through long-term electrocardiogram monitoring is an invaluable tool for diagnosing some cardiac arrhythmias. In such context, providing tools for fast locating alterations of normal conduction patterns is mandatory and still remains an open issue. This paper presents a real-time method for adaptive clustering QRS complexes from multilead ECG signals that provides the set of QRS morphologies that appear during an ECG recording. The method processes the QRS complexes sequentially by grouping them into a dynamic set of clusters based on the information content of the temporal context. The clusters are represented by templates which evolve over time and adapt to the QRS morphology changes. Rules to create, merge, and remove clusters are defined along with techniques for noise detection in order to avoid their proliferation. To cope with beat misalignment, derivative dynamic time warping is used. The proposed method has been validated against the MIT-BIH Arrhythmia Database and the AHA ECG Database showing a global purity of 98.56% and 99.56%, respectively. Results show that our proposal not only provides better results than previous offline solutions but also fulfills real-time requirements.
通过长期心电图监测对心脏状况进行连续随访，是诊断某些心律失常的一种非常有价值的工具。在这种情况下，提供快速定位正常传导模式变化的工具是强制性的，并且仍然是一个悬而未决的问题。本文提出了一种从多导联 ECG 信号中自适应聚类 QRS 复合体的实时方法，该方法提供了在 ECG 记录期间出现的 QRS 形态的集合。该方法通过根据时间上下文的信息量将它们分组到动态簇集中来顺序处理 QRS 复合体。簇由随时间演变并适应 QRS 形态变化的模板表示。创建、合并和删除簇的规则以及用于避免其扩散的噪声检测技术都已定义。为了解决节拍失配问题，使用了导数动态时间规整。针对 MIT-BIH 心律失常数据库和 AHA ECG 数据库对所提出的方法进行了验证，分别达到了 98.56%和 99.56%的全局纯度。结果表明，我们的方法不仅提供了比以前的离线解决方案更好的结果，而且还满足了实时要求。
