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A novel method for ECG signal classification via one-dimensional convolutional neural network

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Abstract

This paper develops an end-to-end ECG signal classification algorithm based on a novel segmentation strategy and 1D Convolutional Neural Networks (CNN) to aid the classification of ECG signals and alleviate the workload of physicians. The ECG segmentation strategy named R-R-R strategy (i.e., retaining ECG data between the R peaks just before and after the current R peak) is used for segmenting the original ECG data into segments to train and test the 1D CNN models. The novel strategy mimics physicians in scanning ECG to a greater extent, and maximizes the inherent information of ECG segments for diagnosis. The performance of the proposed end to end ECG signal classification algorithm was verified with the ECG signals from 48 records in the MIT-BIH arrhythmia database. When the heartbeat types were divided into the five classes recommended by clinicians, i.e., normal beat, left bundle branch block beat, right bundle branch block beat, premature ventricular contraction, and paced beat, the classification accuracy, the area under the curve (AUC), the sensitivity, and the F1-score achieved by the proposed model were 0.9924, 0.9994, 0.99 and 0.99, respectively. When the heartbeat types were divided into six classes recommended by clinicians, i.e., normal beat, left bundle branch block beat, right bundle branch block beat, premature ventricular contraction, paced beat and other beats, the beat classification accuracy, the AUC, the sensitivity, and the F1-score achieved by the model reached 0.9702, 0.9966, 0.97, and 0.97, respectively. When the heartbeat types were divided into five classes recommended by the Association for Advancement of Medical Instrumentation (AAMI), i.e., normal beat, supraventricular ectopic beat, ventricular ectopic beat, fusion beat, and unknown beat, the beat classification accuracy, the sensitivity, and the F1-score were 0.9745, 0.97, and 0.97, respectively. Experimental results show that the proposed method achieves better performance than the state-of-the-art methods.

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Acknowledgements

This work was supported in part by the Key Lab of Computer Networks and Information Integration (Southeastern University), Ministry of Education, China (K93-9-2017-03), by the Department of Education Shaanxi Province, China (15JK1673), and by Shaanxi Provincial Natural Science Foundation of China (2016JM8034, 2018GY-135, 2018ZDXM-GY-091, 2020SF377). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.

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Authors and Affiliations

  1. College of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an, 710121, China

    Xuan Hua

  2. Key Laboratory of Network Data Analysis and Intelligent Processing in Shaanxi, Xi’an, 710121, China

    Xuan Hua

  3. College of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an, 710121, China

    Jungang Han

  4. College of Computing, Michigan Technological University, Houghton, MI, 49931, USA

    Chen Zhao, Zhuo He, Jinshan Tang & Weihua Zhou

  5. School of Computing, University of Southern Mississippi, Long Beach, 39560, USA

    Haipeng Tang

  6. Department of Kinesiology and Integrative Physiology, Michigan Technological University, Houghton, MI, 49931, USA

    Qinghui Chen

  7. School of Automation, Xi’an University of Posts and Telecommunications, Xi’an, 710121, China

    Shaojie Tang

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  1. Xuan Hua
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Correspondence to Jungang Han, Jinshan Tang or Weihua Zhou.

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The ECG data used to support the findings of this study have been deposited in the MIT-BIH repository https://physionet.org/content/mitdb/1.0.0.

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Hua, X., Han, J., Zhao, C. et al. A novel method for ECG signal classification via one-dimensional convolutional neural network. Multimedia Systems 28, 1387–1399 (2022). https://doi.org/10.1007/s00530-020-00713-1

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