Patient-specific ECG classification based on recurrent neural networks and clustering technique

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....

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Bibliographic Details
Published in:2017 13th IASTED International Conference on Biomedical Engineering (BioMed) pp. 63 - 67
Main Authors: Zhang, Chenshuang, Wang, Guijin, Zhao, Jingwei, Gao, Pengfei, Lin, Jianping, Yang, Huazhong
Format: Conference Proceeding
Language:English
Published: International Association of Science and Technology for Development--IASTED 2017
Subjects:
Classification algorithms
Data models
Deep Learning
Density Based Clustering Algorithm
Diseases
ECG Classification
Electrocardiography
Morphology
Recurrent neural networks
Transforms
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Summary: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.
DOI:10.2316/P.2017.852-029