An ECG classification model based on multilead wavelet transform features

The objective of this work is to develop a model for ECG classification based on multilead features. The MIT-BIH Arrhythmia database was used following AAMI recommendations and class labeling. We used for classification classical features as well as features extracted from different scales of the wavelet decomposition of both leads integrated in an RMS manner. Step-wise and a randomized method were considered for feature subset selection, and linear discriminant analysis (LDA) was also used for additional dimensional reduction. Three classifiers: linear, quadratic and Mahalanobis distance were evaluated, using a k-fold like cross validation scheme. Results in the training set showed that the best performance was obtained with a 28-feature subset, using LDA and a Mahalanobis distance classifier. This model was evaluated in the test dataset with the following performance measurements global accuracy: 86%; for supraventricular beats, Sensitivity: 86%, Positive pred.: 20%; for ventricular beats Sensitivity: 71%, Positive pred.: 61%. This results show the feasibility of classification based on the multilead wavelet features, although further development is needed in subset selection and classification algorithms.