JOURNAL BROWSE
Search
Advanced SearchSearch Tips
R-Peak Detection Algorithm in ECG Signal Based on Multi-Scaled Primitive Signal
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
R-Peak Detection Algorithm in ECG Signal Based on Multi-Scaled Primitive Signal
Cha, Won-Jun; Ryu, Gang-Soo; Lee, Jong-Hak; Cho, Woong-Ho; Jung, YouSoo; Park, Kil-Houm;
  PDF(new window)
 Abstract
The existing R-peak detection research suggests improving the distortion of the signal such as baseline variations in ECG signals by using preprocessing techniques such as a bandpass filtering. However, preprocessing can introduce another distortion, as it can generate a false detection in the R-wave detection. In this paper, we propose an R-peak detection algorithm in ECG signal, based on primitive signal in order to detect reliably an R-peak in baseline variation. First, the proposed algorithm decides the primitive signal to represent the QRS complex in ECG signal, and by scaling the time axis and voltage axis, extracts multiple primitive signals. Second, the algorithm detects the candidates of the R-peak using the value of the voltage. Third, the algorithm measures the similarity between multiple primitive signals and the R-peak candidates. Finally, the algorithm detects the R-peak using the mean and the standard deviation of similarity. Throughout the experiment, we confirmed that the algorithm detected reliably a QRS group similar to multiple primitive signals. Specifically, the algorithm can achieve an R-peak detection rate greater than an average rate of 99.9%, based on eight records of MIT-BIH ADB used in this experiment.
 Keywords
ECG;Primitive Signal;R-peak Detection;
 Language
Korean
 Cited by
 References
1.
B. Kohler, C. Henning, and R. Orglmeister, "The Principles of Software QRS Detection," IEEE Engineering in Medicine and Biology, Vol. 21, pp. 42-57, 2002. crossref(new window)

2.
H.J. Jong and J.S. Lim, "Detection of Premature Ventricular Contraction Using Discrete Wavelet Transform and Fuzzy Neural Network," Journal of Korea Multimedia Society, Vol. 12, No. 3, pp. 451-459, 2009.

3.
J.S. Sahambi, S.N. Tandon, and R.K.P. Bhatt, “Wavelet Based ST-Segment Analysis,” Journal of Medical & Biological Engineering & Computing, Vol. 36, No. 5, pp. 568-572, 1998. crossref(new window)

4.
S. Kadambe, R. Murray, and G. Boudreaux-Bartels, "Wavelet Transform Based QRS Complex Detector," IEEE Transactions on Biomedical Engineering, Vol. 46, No. 7, pp. 838-848, 1999. crossref(new window)

5.
E. Zellmer, F. Shang, and H. Zhang "Highly Accurate ECG Beat Classification Based on Continuous Wavelet Transformation and Multiple Support Vector Machine Classifiers," Proceeding of Biomedical Engineering and Informatics Conference MMEI , pp. 1-5, 2009.

6.
T. Ince, S. Kiranyaz, and M. Gabbouj, "Automated Patient-Specific Classification of Premature Ventricular Contractions," Proceeding of 30th Annual International IEEE EMBS Conference, pp. 5474-5477, 2008.

7.
L. Shyu, Y. Wu, and W. Hu, “Using Wavelet Transform and Fuzzy Neural Network for VPC Detection from the Holter ECG,” IEEE Transactions on Biomedical Engineering, Vol. 51, No. 7, pp. 1269-1273, 2004. crossref(new window)

8.
F. Melgani and Y. Bazi, “Detecting Premature Ventricular Contractions in ECG Signals with Gaussian Process,” Computers in Cardiology, Vol. 35, pp. 237-240, 2008.

9.
J. Pan and W. Tompkins, “A Real-Time QRS Detection Algorithm,” IEEE Transactions on Biomedical Engineering, Vol. 32, No. 3, pp. 230-236, 1985. crossref(new window)

10.
W.J. Brady and J. Skiles “Wide QRS Complex Tachycardia: ECG Differential Diagnosis," The American Journal of Emergency Medicine, Vol. 17, No. 4, pp. 376-381, 1999. crossref(new window)

11.
J.J. Kim, J.S. Kim, and K.H. Park, “R-wave Detection Algorithm in ECG Signal Using Adaptive Refractory Period,” Journal of the Institute of Electronics Engineers of Korea, Vol. 50, No. 5, pp. 242-250, 2013.

12.
S.W. Kim, T.H. Kim, B.J. Choi, and K.H. Park, “Mining Algorithm of Baseline Wander for ECG Signal Using Morphology-Pair,” Journal of Korean Institute of Intelligent Systems, Vol. 20, No. 45, pp. 574-579, 2010. crossref(new window)

13.
A. Gautam and M. Kaur, “ECG Analysis Using Continuous Wavelet Transform (CWT),” IOSR Journal of Engineering, Vol. 2, No. 4, pp. 632-635, 2012. crossref(new window)