Clustering analysis strategy of online Ambulatory Electrocardiogram waveforms
ICNC), 2010 Sixth International Conference（2010）
摘要
Two challenges make it difficult to cluster on-line AECG (Ambulatory Electrocardiogram) data. They are huge amount ECG (Electrocardiogram) waveforms and high dimension vector to describe individual ECG waveforms. A new strategy is proposed in the paper, it does not lose the clustering accuracy with reduced the ECG vector dimension data. In reducing ECG vector dimension, Sanger neural network was introduced, its time-consuming is the least among others methods. In clustering analysis, Simulated Annealing algorithm was introduced to improve the effect of clustering result. Through experiments and comparison with other strategy, the proposed strategy reach 94.40% average accuracy rate and nearly 1/4 time consuming.
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关键词
electrocardiography,sanger algorithm,simulated annealing algorithm,ecg vector dimension data,sanger neural network,online ambulatory electrocardiogram waveforms,dimension reduction,simulated annealing,ambulatory electrocardiogram,medical computing,neural nets,clustering analysis strategy,accuracy,principal component analysis,algorithm design and analysis,clustering algorithms,artificial neural networks
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