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

doi:10.3969/j.issn.1006-2475.2019.05.018

Abstract

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

CLC Number:

TN911.23

ZHANG Yi-fan, WANG Hao-ren, SHI Hao-tian, LIU Cheng-liang. Detection of QRS Waves Based on Wavelet Transform and Hilbert Envelope Analysis[J]. Computer and Modernization, 2019, 0(05): 96-.

References

［1］ World Health Organization. Cardiovascular Diseases (CVDs) ［EB/OL］. ［2017-07-05］. http://www.who.int/mediacentre/factsheets/fs317/en.
［2］彭龑,吴兆强,张景,等. 一种改进差分算法及其在QRS波检测中的应用研究［J］. 计算机科学, 2018,45(S1):588-590.
［3］ GHAFFARI A, GOLBAYANI H, GHASEMI M. A new mathematical based QRS detector using continuous wavelet transform［J］. Computers & Electrical Engineering, 2008,34(2):81-91.
［4］丁哨卫,张作生,冯焕清,等. 基于自适应小波变换的QRS波检测算法［J］. 中国科学技术大学学报, 1998(5):580-586.
［5］ ZHU W F, ZHAO H M, CHEN X P. A new QRS detector based on empirical mode decomposition［C］// 2010 IEEE 10th International Conference on Signal Processing. 2010:1757-1760.
［6］ SHIN J H, CHOI B H, LIM Y G, et al. Automatic ballistocardiogram (BCG) beat detection using a template matching approach［C］// 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2008:1144-1146.
［7］ ABIBULLAEV B, SEO H D. A new QRS detection method using wavelets and artificial neural networks［J］. Journal of Medical Systems, 2011,35(4):683-691.
［8］ BOLTON R, WESTPHAL L C. Preliminary results in display and abnormality recognition of Hilbert transformed e.c.g.s［J］. Medical and Biological Engineering and Computing, 1981,19(3):377-384.
［9］ BENITEZ D, GAYDECKI P A, ZAIDI A, et al. The use of the Hilbert transform in ECG signal analysis［J］. Computers in Biology and Medicine, 2001,31(5):399-406.
［10］崔锦泰. 小波分析导论［M］. 程正兴,译. 西安:西安交通大学出版社, 1992.
［11］刘佳昱,黄亦翔,李炳初,等. 基于双正交小波变换QRS波检测方法研究与改进［J］. 机电一体化, 2014(5):21-25.
［12］钟丽辉,魏贯军. 基于Mallat算法的小波分解重构的心电信号处理［J］. 电子设计工程, 2012,20(2):57-59.
［13］吴凡,熊高君,叶志婵. 小波变换在信号突变点检测中的应用［J］. 计算机与现代化, 2008(8):133-135.
［14］王超文,吕扬生,黄玉玺. 用于ECG信号检测与重建的双正交样条小波滤波器［J］. 北京生物医学工程, 2001,20(1):25-28.
［15］朱伟芳,赵鹤鸣,俞一彪. 基于经验模态分解和Hilbert变换的QRS综合波检测算法［J］. 中国生物医学工程学报, 2010,29(3):358-362,372.
［16］王超. ECG去噪和R波检测的算法研究［D］. 合肥:安徽大学, 2018.
［17］陈雪松,赵子豪,张卉雨,等. 改进的自适应小波阈值法及其在测井曲线去噪上的应用［J］. 计算机与现代化, 2014(10):108-111.
［18］FARAHABADI A, FARAHABADI E, RABBANI H, et al. Detection of QRS complex in electrocardiogram signal based on a combination of Hilbert transform, wavelet transform and adaptive thresholding［C］// 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics. 2012:170-173.
［19］KICINSKI W, WHITE C. System for Monitoring Plant Assets Including Machinery: U.S. Patent 6405139［P］. 2002-06-11.
［20］GEORGE B M． MIT-BIT Arrhythmia Database［DB/OL］. （2007-11-05）. http://www. physionet.org/physiobank/database/mitdb/.
［21］PAN J, TOMPKINS W J. A real-time QRS detection algorithm.［J］. IEEE Transactions on Biomedical Engineering, 1985,32(3):230-236.
［22］AFONSO V X, TOMPKINS W J, NGUYEN T Q, et al. ECG beat detection using filter banks［J］. IEEE Transactions on Biomedical Engineering, 1999,46(2):192-202.
［23］行鸿彦,黄敏松. 基于Hilbert-Huang变换的QRS波检测算法研究［J］. 仪器仪表学报, 2009,30(7):1469-1475.

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