基于自适应时间窗函数优化的心电特征波形识别

doi:10.13190/j.jbupt.2018-023

北京邮电大学学报 ›› 2019, Vol. 42 ›› Issue (4): 82-88.doi: 10.13190/j.jbupt.2018-023

基于自适应时间窗函数优化的心电特征波形识别

张金玲, 崔彤, 刘为斌

北京邮电大学电子工程学院, 北京 100876

收稿日期:2018-01-26 出版日期:2019-08-28 发布日期:2019-08-26
作者简介:张金玲(1968-),女,教授,博士生导师,E-mail:zhangjl@bupt.edu.cn.
基金资助:
国家自然科学基金项目（61771063）

Adaptive Time Window Function Optimization Based Electrocardiography Feature Waveform Recognition

ZHANG Jin-ling, CUI Tong, LIU Wei-bin

School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2018-01-26 Online:2019-08-28 Published:2019-08-26

摘要/Abstract

摘要： 提出了自适应加窗小波变换方法，实现了心电特征波形精准定位.依据Q波和S波变化趋势识别出心电波有效起始点和终止点；结合差分阈值法检测QRS起始点，实现了心电特征波段的信息识别.研究结果表明，采用提出的定位识别方法，能对异常和亚健康心电波形进行有效识别，提高对QRS波群、特征P波和T波的识别度，对心电信息的提取和异常性心绞痛、心肌缺血、不稳定型心绞痛等心电异常诊断具有重要意义.

关键词: 自适应加窗小波变换, 差分阈值, 心电信号, 特征波形识别

Abstract: An adaptive windowed wavelet transform method was proposed to make precision positioning of the electrocardiography feature waveforms. The effective starting and ending points of electrocardiography wave were identified according to the trend of Q wave and S wave. The information recognition of the electrocardiography characteristic band was realized by combining the differential threshold method to detect the QRS starting. It is shown that the proposed method can effectively identify abnormal and sub-healthy electrocardiography waveforms and improve the recognition of QRS wave group, P wave and T wave for extration of electrocardiography information and diagnosis of abnormal electrocardiography such as abnomal angina pectoris, myocardial ischemia and unstable angina pectoris.

Key words: adaptive windowed wavelet transform, differential threshold, electrocardiography signal, feature waveform recognition

中图分类号:

TN929.53

张金玲, 崔彤, 刘为斌. 基于自适应时间窗函数优化的心电特征波形识别[J]. 北京邮电大学学报, 2019, 42(4): 82-88.

ZHANG Jin-ling, CUI Tong, LIU Wei-bin. Adaptive Time Window Function Optimization Based Electrocardiography Feature Waveform Recognition[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2019, 42(4): 82-88.

参考文献

[1] Wang L, Zheng C X, Ye J L, et al. A very efficient real-time QRS detection method[J]. Beijing Biomedical Engineering, 1998(4):217-222.
[2] Cheng X M, Lin J S, Zhang Z G. An improved template matching method for high resolution ECG[J]. Chinese Journal of Biomedical Engineering, 1999, 18(1):89-96.
[3] Chen Y Q, Li G, Ye W Y, et al. An improved algorithm of adaptive coherent model in the application of electrocardiograph[J]. Signal Processing, 2002, 18(3):244-248.
[4] 马玉润. ECG预处理与QRS波群检测技术研究[D]. 兰州:兰州大学, 2013.
[5] Liu Z D, Chen J, Tang M F. Pulse transit time detection based on waveform time domain feature and dynamic difference threshold[J]. Journal of Biomedical Engineering, 2017, 34(3):329-334.
[6] Yan H L, An Y, Wang H F, et al. Feature extraction of ECG hearbeats based on convolutional neural networks[J]. Computer Engineering and Design, 2017, 38(4):1024-1028.
[7] Zhang J L, Zheng X. Stationary wavelet transform and new threshold function used in ECG signal denoising[J]. Journal of Beijing University of Posts and Telecommunications, 2017, 40(1):28-31.
[8] Kher R, Vala D, Pawar T, et al. Implementation of derivative based QRS complex detection methods[C]//3^rd International Conference on Biomedical Engineering and Informatics. Yantai:[s. n.], 2010:927-931.
[9] Dong S, Xu F, Lingwood B, et al. R-wave detection:a comparative analysis of four methods using newborn piglet ECG[C]//International Conference on Information Sciences Signal Processing & Their Applications. Kuala Lumpur:[s. n.], 2010:320-323.
[10] Zhang Q L, Su S M, Wang M. QRS feature extration algorithm based on wavelet transform[J]. Journal of Zhengzhou University, 2017, 49(4):100-103.
[11] 姚成. 心电信号智能分析关键技术研究[D]. 长春:吉林大学, 2012.
[12] Yochum M, Renaud C, Jacquir S. Automatic detection of P, QRS and T patterns in 12 leaders ECG signal based on CWT[J]. Biomedical signal processing and control, 2016(25):46-52.
[13] Madeiro J P, Cortez P C, Marques J A, et al. An innovative approach of QRS segmentation based on first-derivative, Hilbert and wavelet transforms[J]. Medical Engineering & Physics, 2012, 34(9):1236-1246.
[14] Wang Z X, Zhang S J, Zeng X P. Shape recognition algorithm for ST-segment of ECG signal[J]. Journal of Computer Applications, 2011, 31(10):2811-2810.
[15] Gao Z, Kong F, Zhang X. Accurate and rapid QRS detection for intelligent ECG monitor[C]//Third International Conference on Measuring Technology and Mechatronics Automation.[S. l.]:IEEE Computer Society, 2011:298-301.
[16] Dai J X, Li Z X, Song H X. The analysis and application of signal Lipschitz exponent based on wavelet[J]. Journal of Nanjing University of Posts ant Telecommunications (Natural Science), 2008, 28(6):69-73,82.
[17] Ma A P, Liu L. Research on fault signal detection of transmission line based on wavelet theory[J]. Sichuan Electric Power Technology, 2010, 30(5):356-361.
[18] 赵毅. 基于小波分析和神经网络的异常心电信号分类研究[D]. 太原:太原理工大学, 2015.
[19] Su L M, Dai Q J, Wang J. R waveform calibration and QRS waveform detection of B-splines-based biorthbogonal wavelets[J]. Journal of Clinical Rehabilitative Tissue Engineering Research, 2009, 13(9):1657-1660.
[20] 马宪. 远程动态心电信号的信息提取与认知[D]. 北京:北京邮电大学, 2013.

基于自适应时间窗函数优化的心电特征波形识别

Adaptive Time Window Function Optimization Based Electrocardiography Feature Waveform Recognition

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 1

编辑推荐

Metrics

本文评价