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QRS detection of ECG signal using U-Net and DBSCAN

  • 1176: Artificial Intelligence and Deep Learning for Biomedical Applications
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Abstract

QRS detection is a crucial task for ECG signal analysis, which is the preliminary and essential step to further recognition and diagnosis. This paper proposes a U-Net based method for QRS detection. The method consists of three steps including preprocessing, U-Net model, and density-based spatial clustering of applications with noise(DBSCAN). The normalization is carried out using the Z-score method in preprocessing. In this study, location prediction is conducted by the U-Net model. Subsequently, the U-Net outputs are thresholded and clustered by DBSCAN. Finally, the middle points of the cluster are regards as the R-peak of the QRS complex. We demonstrate that the proposed method achieving high accuracy on ECG signals from the MIT-BIH Arrhythmia Database(MITDB). The experimental results show an average sensitivity of 99.98 %, positive predictivity of 99.95 %, accuracy of 99.93 %, and F1-score of 99.97 %. Compared with other existing methods, the overall performance is comparable and even better in terms of accuracy and F1-score.

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Acknowledgements

This work is supported by Wenfeng Innovation Foundation of CQUPT, University Innovation Team Construction Plan Funding Project of Chongqing (Smart Medical System and Key Techniques), Chongqing Key Laboratory Improvement Plan (Chongqing Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology), National Natural Science Foundation of China (62001276, 61971079), Guangdong Basic and Applied Basic Research Fundation (2019A1515110560), Chongqing Research Program of Basic Research and Frontier Technology (cstc2017jcyjAX0328),the Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN201800614, KJQN201800643, KJQN202000604), the Regional Creative Cooperation Program of Sichuan (2020YFQ0025), the Innovation Group of Chongqing (cstc2020jcyj-cxttX0002), the Scientific Research Foundation of CQUPT(A2016-73).

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Authors and Affiliations

  1. Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Huiqian Wang, Sijia He, Ting Liu, Yu Pang, Jinzhao Lin & Qinghui Liu

  2. Shantou University, Shantou, 515063, China

    Kaining Han & Junchao Wang

  3. Department of Embedded Systems Engineering, Incheon National University, Incheon, Korea

    Gwanggil Jeon

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  1. Huiqian Wang
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  2. Sijia He
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Correspondence to Junchao Wang or Gwanggil Jeon.

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Wang, H., He, S., Liu, T. et al. QRS detection of ECG signal using U-Net and DBSCAN. Multimed Tools Appl 81, 13319–13333 (2022). https://doi.org/10.1007/s11042-021-10994-x

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  • DOI: https://doi.org/10.1007/s11042-021-10994-x

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