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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 …