自适应CA-CFAR的心电信号R波检测技术

包志强, 罗小宏, 吕少卿, 黄琼丹

包志强, 罗小宏, 吕少卿, 黄琼丹. 自适应CA-CFAR的心电信号R波检测技术[J]. 信号处理, 2019, 35(12): 1959-1968. DOI: 10.16798/j.issn.1003-0530.2019.12.004
引用本文: 包志强, 罗小宏, 吕少卿, 黄琼丹. 自适应CA-CFAR的心电信号R波检测技术[J]. 信号处理, 2019, 35(12): 1959-1968. DOI: 10.16798/j.issn.1003-0530.2019.12.004
Bao Zhiqiang, Luo Xiaohong, Lü Shaoqing, Huang Qiongdan. Adaptive CA-CFAR R wave detection technique for ECG signal[J]. JOURNAL OF SIGNAL PROCESSING, 2019, 35(12): 1959-1968. DOI: 10.16798/j.issn.1003-0530.2019.12.004
Citation: Bao Zhiqiang, Luo Xiaohong, Lü Shaoqing, Huang Qiongdan. Adaptive CA-CFAR R wave detection technique for ECG signal[J]. JOURNAL OF SIGNAL PROCESSING, 2019, 35(12): 1959-1968. DOI: 10.16798/j.issn.1003-0530.2019.12.004

自适应CA-CFAR的心电信号R波检测技术

基金项目: 陕西省教育厅科研计划项目资助(17JK0703);陕西省重点研发计划资助项目(2018GY-150);西安市科技计划项目(201805040YD18CG24-3,GXYD17.5)
详细信息
  • 中图分类号: TN911.72

Adaptive CA-CFAR R wave detection technique for ECG signal

  • 摘要: 针对心电信号R波的突变特性,利用雷达信号的检测方法,本文提出一种自适应单元平均恒虚警率(cell averaging-constant false alarm rate, CA-CFAR)的R波检测方法。首先利用滤波器组对心电信号进行预处理;然后将预处理后的信号利用自适应CA-CFAR检测判决;最后由心电信号R波的间隔特性做一个不应期剔除规则的处理,得到R波的定位。对美国麻省理工学院提供的MIT-BIH数据库中心电图(Electrocardiograph, ECG)信号仿真,实验证明,自适应参考单元的CA-CFAR对MIT-BIH的ECG信号R波检测的精准率为99.842%,检测误差为0.354%。实测数据表明了算法的有效性和适用性。
    Abstract: Aiming at the mutation characteristics of R-wave of ECG signal, a method of R wave detection based on adaptive cell averaging-constant false alarm rate (CA-CFAR) is proposed by using radar signal detection method. Firstly, filter banks are used to preprocess ECG signals; then, adaptive CA-CFAR is used to detect and judge the preprocessed signals; finally, a rule of refractory elimination is processed according to the interval characteristics of R waves of ECG signals, and the location of R waves is obtained. The simulation of electrocardiogram (ECG) signals in MIT-BIH database provided by MIT shows that the accuracy of R-wave detection of ECG signals in MIT-BIH by CA-CFAR of adaptive reference unit is 99.842% and the detection error is 0.354%. The measured data show the effectiveness and applicability of the algorithm.
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出版历程
  • 收稿日期:  2019-09-18
  • 修回日期:  2019-11-08
  • 发布日期:  2019-12-24

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