Vale Institute Of Technology, Ouro Preto, MG, 35400-000, Brazil.
Universidade Federal de Ouro Preto, Computing Department, Ouro Preto, MG, 35400-000, Brazil.
Sci Rep. 2017 Sep 5;7(1):10543. doi: 10.1038/s41598-017-09837-3.
Classifying arrhythmias can be a tough task for a human being and automating this task is highly desirable. Nevertheless fully automatic arrhythmia classification through Electrocardiogram (ECG) signals is a challenging task when the inter-patient paradigm is considered. For the inter-patient paradigm, classifiers are evaluated on signals of unknown subjects, resembling the real world scenario. In this work, we explore a novel ECG representation based on vectorcardiogram (VCG), called temporal vectorcardiogram (TVCG), along with a complex network for feature extraction. We also fine-tune the SVM classifier and perform feature selection with a particle swarm optimization (PSO) algorithm. Results for the inter-patient paradigm show that the proposed method achieves the results comparable to state-of-the-art in MIT-BIH database (53% of Positive predictive (+P) for the Supraventricular ectopic beat (S) class and 87.3% of Sensitivity (Se) for the Ventricular ectopic beat (V) class) that TVCG is a richer representation of the heartbeat and that it could be useful for problems involving the cardiac signal and pattern recognition.
心律失常的分类对人类来说可能是一项艰巨的任务，因此高度期望实现自动化。然而，当考虑到患者间范例时，通过心电图 (ECG) 信号进行完全自动的心律失常分类是一项具有挑战性的任务。对于患者间范例，分类器在未知主体的信号上进行评估，类似于真实世界的情况。在这项工作中，我们探索了一种基于向量心电图 (VCG) 的新型 ECG 表示形式，称为时变向量心电图 (TVCG)，以及用于特征提取的复杂网络。我们还微调了 SVM 分类器，并使用粒子群优化 (PSO) 算法进行特征选择。患者间范例的结果表明，所提出的方法可实现与 MIT-BIH 数据库中的最新技术相当的结果（室上性异位搏动 (S) 类的正预测 (+P) 为 53%，室性异位搏动 (V) 类的敏感性 (Se) 为 87.3%），TVCG 是心跳的更丰富表示形式，它可能对涉及心脏信号和模式识别的问题有用。
