%0 Journal Article
%T 基于Holter心电数据自动诊断的移动医疗服务<br>The Mobile Medical Service Based on the Analysis of Automatic Diagnosis of Holter Electrocardiogram Data
%A 黄东海
%A 李晓亚
%A 涂昌建
%J Hans Journal of Data Mining
%P 60-67
%@ 2163-1468
%D 2016
%I Hans Publishing
%R 10.12677/HJDM.2016.61008
%X <div style=\"text-align:justify;\">
	<span style=\"line-height:1.5;\">本文从心电图(ECG)信号自动诊断的角度切入移动医疗服务，期望以实现ECG自动诊断来带动移动医疗服务的发展。文中重点描述了实现自动诊断过程中面临的主要问题，并结合临床数据进行验证，提出以树结构层次聚类的方式提高ECG模式分类效果。在Holter临床数据上的实验结果表明，树形层次聚类法可以很好的找到数据集中的异常心拍。<br />
In this paper, we consider the mobile medical service from the perspective of diagnosis of elec-trocardiogram (ECG) signal, and expect to promote the development of mobile medical service through the implementation of automatic diagnosis of ECG. We focus on the main problem during the procedure of implementing the automatic diagnosis, which is illustrated with ECG clinical data. And we propose a hierarchical clustering method based on tree structure to improve the results of ECG pattern classification. The simulation results on clinical data collected by smart device such as Holter, reveal that the tree-like hierarchical clustering method can effectively detect abnormal heart beat from ECG data set.</span><span style=\"line-height:1.5;\"></span> 
</div>
%K 心电图，模式分类，自动诊断，移动医疗服务<br>Electrocardiogram
%K Pattern Classification
%K Automatic Diagnosis
%K Mobile Medical Service
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=16830