Detection of congestive heart failure from short-term heart rate variability segments using hybrid feature selection approach

ConclusionsThe results suggest that the method can achieve accurate generalizable models for automated diagnosis of CHF from short-term HRV segments in subjects with very few informative features. The choice of the best features and the classification results are similar between the three best classifiers, so the use of any of them with the proposed method is recommended. Nonlinear and symbolic dynamics features are shown to have an important role in the resulting models. The presented methodology may be useful for first-hand screening for CHF as well as for similar diagnostic or automated detection problems in biomedicine.Graphical abstract
Source: Biomedical Signal Processing and Control - Category: Biomedical Science Source Type: research