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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| Vydáno v: | 2017 13th IASTED International Conference on Biomedical Engineering (BioMed) s. 63 - 67 |
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| Jazyk: | angličtina |
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International Association of Science and Technology for Development--IASTED
2017
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| Abstract | 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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| AbstractList | 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. |
| Author | Wang, Guijin Lin, Jianping Yang, Huazhong Gao, Pengfei Zhang, Chenshuang Zhao, Jingwei |
| Author_xml | – sequence: 1 givenname: Chenshuang surname: Zhang fullname: Zhang, Chenshuang organization: Department of Electronic Engineering, Tsinghua University, Beijing, China – sequence: 2 givenname: Guijin surname: Wang fullname: Wang, Guijin organization: Department of Electronic Engineering, Tsinghua University, Beijing, China – sequence: 3 givenname: Jingwei surname: Zhao fullname: Zhao, Jingwei organization: Department of Electronic Engineering, Tsinghua University, Beijing, China – sequence: 4 givenname: Pengfei surname: Gao fullname: Gao, Pengfei organization: Department of Electronic Engineering, Tsinghua University, Beijing, China – sequence: 5 givenname: Jianping surname: Lin fullname: Lin, Jianping organization: Beijing Xinheyidian Technology Co.,Ltd, China – sequence: 6 givenname: Huazhong surname: Yang fullname: Yang, Huazhong organization: Department of Electronic Engineering, Tsinghua University, Beijing, China |
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| Snippet | In this paper, we propose a novel patient-specific electrocardiogram (ECG) classification algorithm based on the recurrent neural networks (RNN) and density... |
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| SubjectTerms | Classification algorithms Data models Deep Learning Density Based Clustering Algorithm Diseases ECG Classification Electrocardiography Morphology Recurrent neural networks Transforms |
| Title | Patient-specific ECG classification based on recurrent neural networks and clustering technique |
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