A global and updatable ECG beat classification system based on recurrent neural networks and active learning

Abstract

The key challenges faced in the automatic diagnosis of arrhythmia by electrocardiogram (ECG) is enormous differences among individual patients and high cost of labeling clinical ECG records. In order to establish a system with an automatic feature learning scheme and an effective optimization mechanism, we propose a global and updatable classification scheme named Global Recurrent Neural Network (GRNN). Recurrent Neural Network (RNN) is adopted to explore the underlying features of ECG beats, based on morphological and temporal information. In order to improve system performance when new samples are obtained, active learning is applied to select the most informative beats and incorporate them into training set. The system is then updated as the training set grows. Our GRNN has three main innovations. Firstly, relying on the large capacity and fitting ability of GRNN, we can classify samples of multiple different patients with a single model. Secondly, the GRNN improves generalization performance when training samples and test samples are from distinct databases. This can be explained that the optimization mechanism finds the most informative samples to improve performance as training data. Finally, RNN automatically learns the underlying differences among the samples from different classes. Experimental results prove that the GRNN system achieves the state-of-the-art performance with a single model. In across-database experiments where the training data and test data are from different databases respectively, the GRNN achieves significant improvement compared with other algorithms. This study illustrates the feasibility of a global and updatable ECG beat classification system in practical applications.