Conditional mutual information-based feature selection for congestive heart failure recognition using heart rate variability

Feature selection plays an important role in pattern recognition systems. In this study, we explored the problem of selecting effective heart rate variability (HRV) features for recognizing congestive heart failure (CHF) based on mutual information (MI). The MI-based greedy feature selection approach proposed by Battiti was adopted in the study. The mutual information conditioned by the first-selected feature was used as a criterion for feature selection. The uniform distribution assumption was used to reduce the computational load. And, a logarithmic exponent weighting was added to model the relative importance of the MI with respect to the number of the already-selected features. The CHF recognition system contained a feature extractor that generated four categories, totally 50, features from the input HRV sequences. The proposed feature selector, termed UCMIFS, proceeded to select the most effective features for the succeeding support vector machine (SVM) classifier. Prior to feature selection, the 50 features produced a high accuracy of 96.38%, which confirmed the representativeness of the original feature set. The performance of the UCMIFS selector was demonstrated to be superior to the other MI-based feature selectors including MIFSU, CMIFS, and mRMR. When compared to the other outstanding selectors published in the literature, the proposed UCMIFS outperformed them with as high as 97.59% accuracy in recognizing CHF using only 15 features. The results demonstrated the advantage of using the recruited features in characterizing HRV sequences for CHF recognition. The UCMIFS selector further improved the efficiency of the recognition system with substantially lowered feature dimensions and elevated recognition rate. (C) 2012 Elsevier Ireland Ltd. All rights reserved.