Multiclass maximum margin clustering via immune evolutionary algorithm for automatic diagnosis of electrocardiogram arrhythmias
作者： Bohui Zhu , Yongsheng Ding , Kuangrong Hao
DOI: 10.1016/J.AMC.2013.11.028
关键词:
摘要: Maximum margin clustering algorithm can obtain outstanding performance by finding the maximum hyperplanes between clusters that separate data from different classes in an unsupervised way. However, it is only suitable for of small set, since requires solving non-convex integer problem, which computationally expensive. In this paper, to further improve performance, a new multiclass method based on and immune evolutionary (IEMMMC) proposed diagnosis electrocardiogram (ECG) arrhythmias. Five types ECG arrhythmias obtained MIT-BIH database are analyzed experiment, including normal sinus rhythm (N), premature ventricular contraction (PVC), atrial (APC), fusion beat (FVN), paced (FPN). And three evaluation indicators used assess effect IEMMMC arrhythmias, such as sensitivity, specificity accuracy. Compared with K-means, fuzzy c-means LS-SVM algorithms, our reflects better not result but also terms global search ability convergence ability, proves its effectiveness detection
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