Empirical mode decomposition for QRS complexes and T wave detection

被引：2

作者：

Guo X.-M. ^{[1
]}

Tang L.-P. ^{[1
]}

Chen L.-S. ^{[1
]}

Chen M.-M. ^{[1
]}

机构：

[1] College of Bioengineering, Chongqing University, Chongqing 400044, Shapingba

来源：

Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China | 2011年 / 40卷 / 01期

关键词：

Empirical mode decomposition; End extending; QRS complexes; Real-time; T wave;

D O I：

10.3969/j.issn.1001-0548.2011.01.027

中图分类号：

学科分类号：

摘要：

In order to detect the position of QRS and T wave in a non-preprocessed ECG signal, a combination method of the empirical mode decomposition (EMD) and morphological algorithm is introduced in this paper. Firstly, a novel boundary processing method is proposed to decrease the boundary distortion of EMD by means of signal extending. Secondly, the improved EMD is used to decompose the ECG signal into stationary intrinsic mode functions (IMFs) and residual components. Next, the two IMFs of low frequencies are reconstructed after de-noising with threshold method, and then the reconstructed signal is supplied to orient QRS to morphological method. T wave is detected by residual components. This method has been validated by the data from the MIT-BIH database, and the result shows that the detection rate of QRS is up to 99%. Moreover, this method has higher accuracy and better real-time performance compared with the traditional methods.

引用

页码：142 / 146

页数：4

共 17 条

[1]

Martinez J.P., Almeida R., Olmos S., Et al., A wavelet-based ECG delineator evaluation on standard databases, IEEE Trans Biomed Eng, 51, pp. 570-581, (2004)

[2]

Dokur Z., Olmez T., Yazgan E., Et al., Detection of ECG waveforms by neural networks, Med Eng Phys, 8, pp. 738-741, (1997)

[3]

Clavier L., Boucher J.M., Lepage R., Et al., Automatic P-wave analysis of patients prone to atrial fibrillation, Med Biol Eng Comp, 1, pp. 63-71, (2002)

[4]

Chen Z.-C., Bai J.-L., Study of detection method on QRS complexes based on wavelet transform, Chinese Journal of Medical Equipment, 30, 2, pp. 4-6, (2009)

[5]

Huang N.E., Attoh-Okine N.O., The Hilbert-Huang Transform in Engineering, (2005)

[6]

Balocchi R., Menicuccid, Santarcan-Gelo E., Et al., Deriving the respiratory sinus arrhythmia from the heartbeat time series using empirical mode decomposition, Chaos, Solitons and Fractals, 20, 1, pp. 171-177, (2004)

[7]

Echeverria J.C., Crowe J.A., Woolfson M.S., Et al., Application of empirical mode decomposition to heart rate variability analysis, Medical and Biological Engineering and Computing, 39, pp. 471-479, (2001)

[8]

Li X.-J., A QRS detection algorithm based on the empirical mode decomposition, Journal of University of Electronic Science and Technology of China, 36, 4, pp. 795-797, (2007)

[9]

Zhang D.-M., Hao J.-F., Tan G.-J., R wave detection algorithm and noise removal in ECG based on empirical mode decomposition, Computer Engineering and Design, 28, 1, pp. 217-219, (2007)

[10]

Qin A.-N., Gui W.-H., Tang G.-D., A etection method of modified QRS complexes, Journal of Center South University (Science and Technology), 39, 5, pp. 1050-1052, (2008)

← 1 2 →