A novel combination clustering and MR control chart method improves the automated recognition and clustering of multiscale entropy curves in ECG signal analysis.
Higher complexities of multiscale entropy (MSE) curve present the physiological system has the better ability to adapt under environment change. Traditional way to distinguish different complexity groups of MSE curves according to the area under MSE curves (AUC) by human self-determination, but that would be difficult to judge when some curves had similar AUC or had overlapped. This paper proposed a combination clustering and MR control chart to calculate the group distances as the response to assessment the clustering result of different MSE curves combination. From the experiment analysis result for ECG signal, using the four features considered in this paper could provide a good recognition in cluster MSE curves.
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Wang et al. (2012) studied this question.
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