SA  >> Vol. 2 No. 4 (December 2013)

    基于函数系数非参数统计模型的心电R波检测
    R Wave Detection in ECG with Functional Coefficient Nonparametric Statistical Model

  • 全文下载: PDF(1945KB) HTML    PP.127-135   DOI: 10.12677/SA.2013.24019  
  • 下载量: 2,451  浏览量: 8,948   科研立项经费支持

作者:  

孙 敏,苏理云,李晨龙:重庆理工大学数学与统计学院,重庆

关键词:
心电信号R波函数系数非参数统计模型自适应Electrocardiogram (ECG) Signal; R Wave; Functional Coefficient Nonparametric Statistical Model; Adaptive Method

摘要:

本文提出了一种基于函数系数非参数统计模型的R波检测方法。在用数字带通滤波器对心电信号进行滤波和去噪以后,用函数系数非参数回归模型来拟合心电时域数据,通过差分选取适当的阈值,并且所选取的阈值具有自适应学习的特性,可根据不同需求调整阈值的大小,然后利用模型估计出的一阶导数位置检测出R波。和差分阈值的方法相比较,该方法在检测R波的过程中对数据本身就有一个很好的平整作用。最后还分别讨论了R波的多检和漏检的情况,并给出了相应的纠正方法以使得检测更加精确。我们利用临床试验数据对该方法进行验证,实验结果表明,该方法能够准确高效地检测出心电信号中的R波。

A novel R wave detection method is presented based on functional coefficient nonparametric statistical model. Firstly, digital bandpass filter is used for denoising and filtering of Electrocardiogram (ECG) signal. Then ECG signal is fitted by using functional coefficient nonparametric statistical model and difference threshold. Finally, the derivative of fitting model is applied to detect R wave in ECG with signal processing and threshold. Compared to the old difference method for R wave detector, the nonparametric statistical regression approach is more accurate based on the derivative of each point. Clinical data are used to test the performances. The experimental results show that the proposed method for R wave detection is effective.

文章引用:
孙敏, 苏理云, 李晨龙. 基于函数系数非参数统计模型的心电R波检测[J]. 统计学与应用, 2013, 2(4): 127-135. http://dx.doi.org/10.12677/SA.2013.24019

参考文献

[1] 苏丽, 赵国良, 李东明 (2005) 心电信号QRS波群检测算法研究. 哈尔滨工程大学学报, 4, 513-517.
[2] 张胜, 吴仲光, 李征 (2008) 一种自适应R波检测算法实现.四川大学学报(自然科学版), 3, 498-502.
[3] Friesen, G.M., Jannett, T.C., Jadallah, M.A., Yates, S.L., Quint, S.R. and Nagle, H.T. (1990) A comparison of the noises sensitivity of nine QRS detection algorithms. IEEE Transactions on Biomedical Engineering, 37, 85-98.
[4] 苏理云, 孔彤, 唐华, 李姣军, 陈兵 (2010) 淹没在分形噪声中的心电信号R波检测. 重庆理工大学学报(自然科学), 4, 86-89.
[5] Saxena, S.C., Kumar, V. and Hamde, S.T. (2002) QRS detection using new wavelets. Journal of Medical Engineering and Technology, 26, 7-15.
[6] Legarreta, I.R. and Addison, P.S., et al. (2003) R-wave detection using continuous wavelet modulus maxima. Computers in Cardiology, 30, 565-568.
[7] Talbi, M., Aouinet, A., Salhi, L. and Cherif, A. (2011) New method of R-wave detection by continuous wavelet transform. Signal Processing: An International Journal (SPIJ), 5, 165-173.
[8] Cui, W.L. (1995) Detection of ECG characteristic points using wavelet transforms. IEEE Transaction on Biomedical Engineering, 42, 21-28.
[9] Mallat, S. (1989) A theory of multiresolution signals decomposition: The wavelet transform. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 11, 674-693.
[10] Afonso, V.X, Tompkins, W.J., Nguyen, T.Q. and Luo, S. (1999) ECG beat detection using filter banks. IEEE Transactions on Biomedical Engineering, 46, 192-202.
[11] Chen, H.C. and Chen, S.W. (2003) A moving average based filtering system with its application to real-time QRS detection. Computers in Cardiology, 30, 585-588.
[12] 李小燕, 王涛, 冯焕清, 詹长安 (2000) 基于小波变换的自适应滤波器消除ECG中基线漂移. 中国科学技术大学学报, 4, 450-454.
[13] Szilagyi, S.M, Szilagyi, L. and David, L. (1997) Comparison between neural network based adaptive filtering and wavelet transform for ECG characteristic points detection. Engineering in medicine and biology society. Proceedings of the 19th Annual International Conference of the IEEE, 6, 272-2274.
[14] Chen, X.M., Lin, J.S. and Zhang, Z.G. (1999) An improved template matching method for high resolution ECG. Chinese Journal of Biomedical Engineering, 18, 89-96.
[15] 田絮资, 杨建, 黄力宇 (2012) 心电信号去噪的数学形态学滤波器. 计算机工程与应用, 2, 124-126.
[16] Trahanias, P.E. (1993) An approach to QRS complex detection using mathematical morphology. IEEE Transactions on Biomedical Engineering, 40, 201-205.
[17] Fan, J.Q. and Qi, W.Y. (2003) Nonlinear time series: Nonparametric and parametric methods. Springer Series in Statistics. Springer, Berlin.
[18] Su, L.Y. (2010) Prediction of multivariate chaotic time series with local polynomial fitting, Computers Mathematics with Applications, 59, 737-744.
[19] Su, L.Y., Zhao, Y.Y., Yan, T.S. and Li, F.L. (2012) Local polynomial estimation of heteroscedasticity in a multivariate linear regression model and its applications in economics. Plos One, 7, e43719.
[20] Su, L.Y. (2010) Prediction of multivariate chaotic time series with local polynomial fitting. Journal Computers & Mathematics with Applications, 59, 737-744.
[21] Su, L.Y. (2011) Multivariate local polynomial regression with application to Shenzhen component index. Discrete Dynamics in Nature and Society, 2011, 930958.