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

机译：基于经常性神经网络和积极学习的全球和可更新的ECG分类系统

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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 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. (C) 2018 Published by Elsevier Inc.
机译：通过心电图（ECG）自动诊断心律失常（ECG）面临的关键挑战是个体患者的巨大差异，以及标记临床心电图记录的高成本。为了建立具有自动特征学习方案和有效优化机制的系统，我们提出了一种名为全局复发神经网络（GRNN）的全局和可更新的分类方案。采用经常性神经网络（RNN）根据形态学和时间信息探索ECG节拍的潜在特征。为了提高系统性能，当获得新的样本时，应用主动学习以选择最具信息性的节拍并将其整合到培训集中。然后在训练集的增长时更新系统。我们的Grnn有三个主要的创新。首先，依靠GRNN的大容量和拟合能力，我们可以对多种不同患者的样本进行分类单一模型。其次，GNN在训练样本和测试样本来自不同数据库时提高了泛化性能。这可以解释，优化机制找到最具信息丰富的样本，以提高性能作为培训数据。最后，RNN自动学习来自不同类别的样本之间的潜在差异。实验结果证明，GRNN系统通过单一模型实现最先进的性能。在跨数据库实验中，培训数据和测试数据分别来自不同数据库，与其他算法相比，GNN实现了显着的改进。本研究说明了在实际应用中全球和可更新的ECG击败分类系统的可行性。 （c）2018年由elsevier公司发布

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