基于ARMA模型的心电聚类算法

doi:10.3969/j.issn.0258-8021.2012.06.003

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Abstract Feature extraction was one of the important parts in electrocardiogram (ECG) clustering. In this article, a simple autoregressive-moving average (ARMA) was applied to fit the ECG. we used the fitting coefficients'Euclidean Distance as dissimilarity to cluster ECG. However contribution of each dimension feature of data sample to the clustering was not considered in that algorithm. Therefore, we further took the contribution which was obtained by the firstly clustering as the weight of each dimension feature. After each dimension coefficient was weighted, we clustered these data again. The PVC and NSR data obtained from MIT-BIH Arrhythmia Database, was used for experimentation. The results showed that cluster precision reached to 93.10%, which proved effectiveness of the proposed method.

Key words： clustering auto-regressive and moving average(ARMA) model feature extraction weight definition electrocardiogram (ECG)

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	MAO XueMin^* ZHANG TingTing CAI ChuanXi LI Qiong

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MAO XueMin^* ZHANG TingTing CAI ChuanXi LI Qiong. ECG Clustering Algorithm Based on ARMA[J]. journal1, 2012, 31(6): 816-821.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2012.06.003 OR http://cjbme.csbme.org/EN/Y2012/V31/I6/816

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