Selection of optimal features for classification of electrocardiograms

Abstract

The forward sequential selection and backward sequential rejection algorithms and the optimal branch and bound algorithm were evaluated in selection of features for classification of electrocardiograms of 237 patients with old myocardial infarction and 299 subjects without infarction. The branch and bound algorithm proved suitable for small sets of ECG features. However, the computational effort required was orders of magnitude greater than that for the other two methods and became prohibitive with large features sets. A satisfactory and consistent overall classification accuracy was achieved by using the sequential selection algorithms for selecting continuous features by maximizing the Mahalanobis distance at each step of the feature selection process. Maximization of the association index can produce better results but requires more computing effort. Feature selection based on maximizing sensitivity at each step for a fixed level of specificity is less satisfactory when a high level of specificity is required.