ECG beat classification using neural classifier based on deep autoencoder and decomposition techniques

This paper proposes an ECG beat classification system based on deep autoencoder as feature extractor and a system of multiple neural networks as classifier. The objectives are as follows: First is simplifying the feature extraction step by applying the deep autoencoder, which permits defining high level features without neither pre-processing stage nor expert intervention. Second is enhancing the classification performance by decomposing the original multi-class problem into simpler binary subproblems and solving them using independent classifiers. Third is overcoming the problem of imbalanced data, by applying an oversampling method after the decomposition of the original problem. This allows adding synthetic samples according the number of training instances in each subproblem. To evaluate the proposed system, we conduct experiments on MIT-BIH arrhythmia dataset and we consider the recommendations of the Association for the Advancement of Medical Instrumentation, which defines five classes of interest. Furthermore, we perform two types of tests, i.e. intra- and inter-patient, and compare the obtained results with some of the state-of-the-art methods. We show that solving each subproblem independently can enhance the accuracy, sensitivity and specificity.