Browse > Article
http://dx.doi.org/10.5762/KAIS.2011.12.10.4443

Study on R-peak Detection Algorithm of Arrhythmia Patients in ECG  

Ahn, Se-Jong (Department of Computer Engineering, Korea Polytechnic University)
Lim, Chang-Joo (Department of Game&Multimedia Engineering, Korea Polytechnic University)
Kim, Yong-Gwon (Department of Radiological Science, Konyang University)
Chung, Sung-Taek (Department of Computer Engineering, Korea Polytechnic University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.12, no.10, 2011 , pp. 4443-4449 More about this Journal
Abstract
ECG consists of various types of electrical signal on the heart, and feature point of these signals can be detected by analyzing the arrhythmia. So far, feature points extraction method for the detection of arrhythmia done in the many studies. However, it is not suitable for portable device using real time operation due to complicated operation. In this paper, R-peak were extracted using R-R interval and QRS width informations on patients. First, noise of low frequency bands eliminated using butterworth filter, and the R-peak was extracted by R-R interval moving average and QRS width moving average. In order to verify, it was experimented to compare the R-peak of data in MIT-BIH arrhythmia database and the R-peak of suggested algorithm. As a results, it showed an excellent detection for feature point of R-peak, even during the process of operation could be efficient way to confirm.
Keywords
Arrhythmia; R-R Interval; QRS width; R-peak;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 N.V. Thakor, et, al. "Applications of Adaptive Filtering to ECG Analysis: Noise Cancellation and Arrhythmia Detection", IEEE Transactions on Biomedical Engineering, Vol. 38, No. 8, pp.785-794, 1991.   DOI
2 P. S. Hamilton, et, al. "Quantitative Investigation of QRS Detection Rules Using the MIT/BIH Arrhythmia Database", IEEE Transactions on BioMedical Engineering, Vol. BME-33, No 12, pp.1157-1165, 1986.   DOI   ScienceOn
3 V.X. Afonso, et al., "Detecting ventricular fibrillation", IEEE Engineering in Medicine and Biology Society, vol. 14, Issue 2, pp.152-159, 1995.   DOI
4 W. J. Brady, et al., "Wide QRS Complex Tachycardia: ECG Differential Diagnosis", The American Journal of Emergency Medicine, Vol. 17, No. 4, pp.376-381, 1999.   DOI
5 S.E. Dobbs, et, al.. "QRS Detection By Template Matching Using Real-Time Correlation On A Microcomputer", Journal of Clinical Engineering, Vol. 9, No. 3, pp.197-212, 1984.   DOI
6 D.L. Pierce, et, al., "Fast Fourier Transformation of the Entire Low Amplitude Late QRS Potential to Predict Ventricular Tachycardia", Journal of the American College of Cardiology, Vol. 14, No. 7, pp.1731-1740, 1989.   DOI
7 Gary M. Friensen, et al., "A Comparison of the Noise Sensitivity of Nine QRS Detection Algorithms", IEEE Transactions on Biomedical Engineering, Vol.37, No. 1, pp.85-98, 1990.   DOI
8 D.S. Benitez, et, al, "A New QRS Detection Algorithm Base on the Hilbert Transform", Computers in Cardiology of IEEE, vol.27 pp.379-382, 2000.
9 S.K. Kil, et, al., "Recognition of Feature points in ECG and Human Pulse using Wavelet Transform", The Korean Institute of Electrical Engineers, Vol.55, No.2, pp.75-81, 2006.   과학기술학회마을
10 H.J. Chung, et, al. "A Study on R-peak Detection algorithm in ECG", Korea Multimedia Society, Vol.13, No.1, pp.438-441, 2010.