Heart failure discrimination using matching pursuit decomposition

Congestive heart failure (CHF) is a cardiac disease associated with the decreases in cardiac output. As a measure to predict sudden death, we propose a framework for discriminating CHF subjects from normal sinus rhythm (NSR). This framework relies on matching pursuit decomposition to derive a set of features, which are tested in a hybrid genetic algorithm and k-nearest neighbor classifier to select the best feature subset. The performance of the proposed framework is analyzed using both Fantasia and CHF database from Physionet archives which are, respectively, composed of 40 NSR volunteers and 29 CHF subjects. The proposed methodology reaches an overall accuracy of 100% when the features are normalized and the feature subset selection strategy is applied. We believe that our method can be extremely useful to the clinician in primary health care as a support tool to discriminate healthy from CHF subjects.