Feature Selection Scheme Based on Multi-time-scales for Analyzing Congestive Heart Failure

This paper proposed a feature selection method combined with multi-time-scales analysis and heart rate variability (HRV) analysis for middle and early diagnosis of congestive heart failure (CHF). In previous studies regarding the diagnosis of CHF, researchers have tended to increase the variety of HRV features by searching for new ones or to use different machine learning algorithms to optimize the classification of CHF and normal sinus rhythms subject (NSR). In fact, the full utilization of traditional HRV features can also improve classification accuracy. The proposed method constructs a multi-time-scales feature matrix according to traditional HRV features that exhibit good stability in multiple time-scales and differences in different time-scales. The multi-scales features yield better performance than the traditional single-time-scales features when the features are fed into a support vector machine (SVM) classifier, and the results of the SVM classifier exhibit a sensitivity, a specificity, and an accuracy of 99.52%, 100.00%, and 99.83%, respectively. These results indicate that the proposed feature selection method can effectively reduce redundant features and computational load when used for automatic diagnosis of CHF.