Abstract
A new method for clustering analysis of QRS complexes is proposed. The method integrates principal component analysis (PCA) with self-organizing map neural network (SOM). The QRS complex feature is extracted based on PCA and the unsupervised SOM is employed to cluster the data. The characteristics and the behavior of the proposed method applying different SOM architectures are studied. The method is tested with the MIT-BIH database. It is demonstrated that QRS complexes feature can be presented by four largest principle components and the PCA results can be used to cluster analysis efficiently. The relationship between cluster results and clinical categories are also investigated in this paper.
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