Patient-Specific ECG Classification Based on Recurrent Neural Networks and Clustering Technique

Chenshuang Zhang, Guijin Wang, Jingwei Zhao, Pengfei Gao, Jianping Lin, and Huazhong Yang


ECG Classification, Deep Learning, Recurrent Neural Networks, Density Based Clustering Algorithm


In this paper, we propose a novel patient-specific electrocardiogram (ECG) classification algorithm based on the recurrent neural networks (RNN) and density based clustering technique. We use RNN to learn time correlation among ECG signal points and to classify ECG beats with different heart rates. Morphology information including the present beat and the T wave of former beat is fed into RNN to learn underlying features automatically. Clustering method is employed to find representative beats as the training data. Evaluated on the MIT-BIH Arrhythmia Database, the experimental results show that proposed algorithm achieves the state-of-the-art classification performance.

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