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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Veröffentlicht in:	2017 13th IASTED International Conference on Biomedical Engineering (BioMed) S. 63 - 67
Hauptverfasser:	Zhang, Chenshuang, Wang, Guijin, Zhao, Jingwei, Gao, Pengfei, Lin, Jianping, Yang, Huazhong
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	International Association of Science and Technology for Development--IASTED 2017
Schlagworte:	Classification algorithms Data models Deep Learning Density Based Clustering Algorithm Diseases ECG Classification Electrocardiography Morphology Recurrent neural networks Transforms
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Beschreibung
Zusammenfassung:	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