SVD and SVM based approach for congestive heart failure detection from ECG signal

A detection approach integrating R-R interval features extraction and classification for congestive heart failure (CHF) is presented in this paper. In R-R interval features extraction, we use empirical mode decomposition (EMD) to decompose each subject´s R-R interval signal into several intrinsic mode functions (IMF), and use singular value decomposition (SVD) to extract the ranked singular values for each subject´s IMF. The ranked singular values obtained are input to the support vector machine (SVM) for classification of physiological state (health or CHF). The classification results showed that, based on a dataset from the “PhysioNet” website, the total accuracy of using significant ranks´ singular values as features was 89%. These results indicate a promising method of combining SVD and SVM in distinguishing healthy persons from CHF patients.