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Clustering ECG heartbeat using improved semi-supervised affinity propagation

机译：使用改进的半监督亲和力传播对ECG心跳进行聚类

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摘要

The electrocardiogram (ECG) has become an important tool for the diagnosis of cardiovascular diseases. As long-term ECG recordings become more common, driven partly by the development of intelligent hardware, the requirement for automatic ECG analysis continues to grow. Research has attempted to use the expert knowledge to optimise ECG-related algorithms, however, visual analysis of long-term ECG is tedious and operator dependent. In previous studies, an ECG beat clustering approach based on self-organising maps has been applied to reduce the amount of time the operator must to spend. This unsupervised approach partitions the ECG beats into 25 groups, however, the cluster number (25) does not accurately reflect the actual number of categories. In this study, an integrated method is presented for the clustering of ECG beats based on an improved semi-supervised affinity propagation algorithm with independent component analysis. Using the MIT-BIH arrhythmia database, the authors find that the resulting clusters to exhibit a high degree of precision. The integrated method outperforms other conventional methods in the MIT-BIH database, and has great theoretical and practical significance in the field of cardiac disease.
机译：心电图（ECG）已成为诊断心血管疾病的重要工具。随着长期心电图记录变得越来越普遍，部分是由于智能硬件的发展，自动心电图分析的需求持续增长。研究试图利用专家知识来优化与ECG相关的算法，但是，长期ECG的可视化分析很繁琐且取决于操作员。在以前的研究中，已采用基于自组织图的ECG搏动聚类方法来减少操作员必须花费的时间。这种无监督的方法将心电图节拍分为25个组，但是，群集编号（25）不能准确反映类别的实际数量。在这项研究中，提出了一种基于独立分量分析的改进的半监督亲和力传播算法的心电图心律聚类的集成方法。使用MIT-BIH心律失常数据库，作者发现所得簇具有较高的精确度。该综合方法优于MIT-BIH数据库中的其他常规方法，在心脏病领域具有重要的理论和实践意义。

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