Abstract

In this study, the comparative techniques have been developed to perform features extraction for the regression of the ECG images. Two regression methods have been used that are the linear and nonlinear regression. The features extraction techniques developed in this study are the nonnegative matrix factorization used to extract the feature from the ECG images and compare the results with different techniques such as principal component analysis, kernel principal component analysis and independent principal component analysis. These features are used for image regression using two regression techniques and compare between these two regressions techniques. The performance evaluation through this comparison is the error rate that is the root mean square error between the actual data and the data predicted from the regression and the results conclude the principal component analysis technique outperforms the other techniques.

Keywords:

Independent component analysis, kernel principal component analysis, linear regression, non linear regression, nonnegative matrix factorization, principal component analysis,

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Competing interests

The authors have no competing interests.

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The authors have no competing interests.