An Automatic ECG Signal Quality Assessment Method Based on Resnet and Self-Attention

被引:5
|
作者
Liu, Yuying [1 ,2 ]
Zhang, Hao [1 ]
Zhao, Kun [1 ]
Liu, Haiyang [1 ]
Long, Fei [1 ]
Chen, Liping [1 ]
Yang, Yaguang [1 ]
机构
[1] Chinese Acad Sci, Inst Microelect, Beijing 100029, Peoples R China
[2] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2023年 / 13卷 / 03期
基金
国家重点研发计划;
关键词
signal quality assessment; electrocardiogram (ECG); deep learning; Resnet18; attention mechanism; healthcare; binary classification; CLASSIFICATION; SYSTEM;
D O I
10.3390/app13031313
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Electrocardiogram (ECG) signals are among the significant physiological signals that indicate the essential properties of the human body. In recent years, the measurement of ECG signals has become more portable thanks to the increasing usage of wearable health testing technology. However, the enormous amount of signal data gathered over a long period of time does impose a heavy load on medical professionals. In addition, false alarms might occur due to the potential for the detected signal to become jumbled with noise and motion perturbations. Therefore, analyzing the quality of the measured raw ECG signal automatically is a valuable task. In this paper, we propose a new single-channel ECG signal quality assessment method that combines the Resnet network structure and the principle of self-attention to extract ECG signal features using the principle of similarity between individual QRS heartbeats within a time slice of ten seconds. In addition, an improved self-attention module is introduced into the deep neural network to learn the similarity between features. Finally, the network distinguishes between acceptable and unacceptable ECG segments. The model test results indicate that the F1-score can approach 0.954, which leads to a more accurate assessment of the ECG signal quality.
引用
收藏
页数:15
相关论文
共 50 条
  • [1] Pest Identification Based on Fusion of Self-Attention With ResNet
    Hassan, Sk Mahmudul
    Maji, Arnab Kumar
    [J]. IEEE ACCESS, 2024, 12 : 6036 - 6050
  • [2] Finger Vein Recognition Based on ResNet With Self-Attention
    Zhang, Zhibo
    Chen, Guanghua
    Zhang, Weifeng
    Wang, Huiyang
    [J]. IEEE ACCESS, 2024, 12 : 1943 - 1951
  • [3] DMAM-ECG: A Diffusion Model with Self-Attention Module for ECG Signal Denoising
    Hu, Zheng-Dong
    Hong, Yang
    Huang, Jia-Yan
    Chen, Kai-Hong
    Zhao, Wan-Qi
    Grau, Antoni
    Guerra, Edmundo
    Wang, Chuan-Sheng
    Zhang, Fu-Quan
    [J]. Journal of Network Intelligence, 2024, 9 (03): : 1278 - 1296
  • [4] Electrocardiogram signal classification based on fusion method of residual network and self-attention mechanism
    Yuan C.
    Liu Z.
    Wang C.
    Yang F.
    [J]. Shengwu Yixue Gongchengxue Zazhi/Journal of Biomedical Engineering, 2023, 40 (03): : 474 - 481
  • [5] VT/VF Detection Method Based on ECG Signal Quality Assessment
    Jovanovic, Borisav
    Milenkovic, Srdan
    Pavlovic, Milan
    [J]. JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS, 2018, 27 (11)
  • [6] Auxiliary Information Guided Self-attention for Image Quality Assessment
    Yang, Jifan
    Wang, Zhongyuan
    Wang, Guangcheng
    Huang, Baojin
    Yang, Yuhong
    Tu, Weiping
    [J]. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS, 2024, 20 (04)
  • [7] Semantic Segmentation Method of Point Cloud in Automatic Driving Scene Based on Self-attention Mechanism
    Wang D.
    Shang H.
    Cao J.
    Wang T.
    Xia X.
    Han Y.
    [J]. Qiche Gongcheng/Automotive Engineering, 2022, 44 (11): : 1656 - 1664
  • [8] Multimodal Fusion Method Based on Self-Attention Mechanism
    Zhu, Hu
    Wang, Ze
    Shi, Yu
    Hua, Yingying
    Xu, Guoxia
    Deng, Lizhen
    [J]. WIRELESS COMMUNICATIONS & MOBILE COMPUTING, 2020, 2020 (2020):
  • [9] An intelligent diagnostic method of ECG signal based on Markov transition field and a ResNet
    Ji, Lipeng
    Wei, Zhonghao
    Hao, Jian
    Wang, Chunli
    [J]. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2023, 242
  • [10] Refined Self-Attention Transformer Model for ECG-Based Arrhythmia Detection
    Tao, Yanyun
    Xu, Biao
    Zhang, Yuzhen
    [J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2024, 73 : 1 - 14