文章基本信息

标题：Study of Hybrid Strategy for Ambulatory ECG Waveform Clustering
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作者：Zheng, Gang ; Yu, Tian
期刊名称：Journal of Software
印刷版ISSN：1796-217X
出版年度：2011
卷号：6
期号：7
页码：1257-1264
DOI：10.4304/jsw.6.7.1257-1264
语种：English
出版社：Academy Publisher
摘要：A hybrid strategy has been proposed to reduce the wrong clustering on Ambulatory ECG (electrocardiogram). Since Ambulatory ECG is usually composed by 24 hours data, the number of individual ECG waveform can reach to 100,000, the request for accurate clustering result is highly required. The proposed strategy adopted some intelligent algorithms to solve the above problem. It clusters ECG waveform sample (selected from Ambulatory ECG) synchronously by Max-Min distance clustering algorithm, K -means algorithm and Simulated annealing algorithm first. And then, it adopted all three outputs from the above three algorithms as input on Back Propagation Artificial Neural Network (BP ANN). In the end, we got more accurate clustering result from the output of ANN. For testing the results, data of MIT/BIH arrhythmia database were used for experiments. After the controlled trial on MIT/BIH data, it can be safely concluded that the clustering result achieved by improved strategy can got more accurate than that by the traditional clustering algorithm. An average accuracy ratio is about 94.6%, 1.6% higher than k -means algorithm averagely and 1.3% higher than Simulated Annealing algorithm averagely.
关键词：ambulatory electrocardiogram;k-means;artificial neural networks;clustering algorithm;Simulated Annealing algorithm