计算机与现代化

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基于小波变换和希尔伯特包络分析的QRS波检测算法

  

  1. (上海交通大学机械科学与动力工程学院,上海200240)
  • 收稿日期:2018-11-12 出版日期:2019-05-14 发布日期:2019-05-14
  • 作者简介:张异凡(1995-),男,河南信阳人,硕士研究生,研究方向:数据挖掘,工业大数据,E-mail: 183240719@qq.com; 王浩任(1991-),男,博士,研究方向:信号处理与特征提取; 史浩天(1994-),男,博士,研究方向:心电信号分析和机器学习; 刘成良(1964-),男,教授,博士,研究方向:远程监控及设备智能维护。
  • 基金资助:
    国家重点研发计划项目(2017YFB1302004)

Detection of QRS Waves Based on Wavelet Transform and Hilbert Envelope Analysis

  1. (School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)
  • Received:2018-11-12 Online:2019-05-14 Published:2019-05-14

摘要: 提出一种基于双正交小波变换和Hilbert变换的QRS波检测算法。首先,通过双正交小波变换分解与重构,消除高频噪声,同时突出R峰位置,构造出有利于QRS波检测的检测层。然后,对信号求差分和希尔波特变换,进一步抑制P波、T波以及基线漂移等噪声。最后,在计算得到的包络信号上根据自适应阈值及决策规则进行R峰检测。根据MIT-BIH心率失常数据库有标注的临床数据进行验证,QRS波检测结果准确率达到99.01%,同时算法具有不错的鲁棒性和实时性。

关键词: QRS波检测, 心电图, 小波变换, 差分, 希尔伯特变换, 自适应阈值

Abstract: This paper presents an algorithm for QRS detection using biorthogonal wavelet transform and Hilbert transform. First, this algorithm eliminates the high frequency noise and highlights the R peak position through biorthogonal wavelet transform decomposition and reconstruction, and constructs the detection layer which is beneficial to QRS waves detection. Then a technique with differentiation and Hilbert transform is applied on the re-composed signal in order to suppress the effects of P and T waves and decrease the low frequency noise. Finally, R peak is detected according to the adaptive threshold and decision rules. The MIT-BIH arrhythmia dataset is used to verify the performance of the detection method. The accuracy of QRS wave detection results is 99.01%, and the algorithm has good robustness and real-time performance.

Key words: QRS waves detection, ECG, wavelet transform, differentiation, Hilbert transform, adaptive threshold

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