Automatic pattern recognition of ECG signals using entropy-based adaptive dimensionality reduction and clustering

Display Omitted Developed a new entropy-based principal component analysis approach (EPCA) for the dimensionality reduction. It can automatically select the best number of principal components of the PCA.Designed a fuzzy-entropy based clustering measure (ECM) which combines the symmetric fuzzy cross entropy with the average fuzzy entropy of membership matrix.Proposed a novel fuzzy entropy c-means clustering algorithm(FECM) which can adaptively identify the optimal number of clusters based on the ECM.Experiments on ECG signals verifies that the EPCA performs better than PCA schemes based on cumulative percentage and screen graph. The clustering accuracy of FECM is superior to NJW, HCA and K-means. In order to automatically recognize the patterns of ECG signals for different subjects, a new entropy-based principal component analysis (EPCA) is developed in this paper for dimensionality reduction of ECG signals. With the EPCA, Pb, the best number of principal components for a specific subject, is automatically determined by the energy to entropy ratio of reconstructed ECG signals after dimensionality reduction. Then, a novel fuzzy-entropy based c-means clustering (FECM) is proposed for cluster partition of ECG feature data. The optimal number of clusters, i.e. kb for a data set with specific ECG feature is found through a fuzzy-entropy based clustering measure (ECM), in which the average symmetric fuzzy cross entropy of membership subset pairs is combined with the average fuzzy entropy of clusters. Afterwards, the ECG signals in the MIT-BIH Arrhythmia Database are used for performance evaluation of EPCA and FECM. Five indices of signal reconstruction are employed to evaluate the results of dimensionality reduction, which shows that the EPCA performs better than the schemes based on cumulative percentage and scree graph when searching for Pb. Moreover, by comparing ECM with the other eight fuzzy clustering indices, i.e. PC, PE, MPC, XB, FS, Kwon, FHV and PBMF, the ECM has demonstrates the superiority in searching for kb. By using ECM, the adaptability of FECM to various ECG data sets has been strengthened. Its clustering accuracy is superior to those of three commonly-used algorithms, i.e. spectral clustering (NJW), hierarchical agglomerative (HCA) and K-means clustering.