Browse > Article
http://dx.doi.org/10.6109/jkiice.2014.18.4.825

PVC Classification based on QRS Pattern using QS Interval and R Wave Amplitude  

Cho, Ik-Sung (Department of IT Engineering, Pusan National University)
Kwon, Hyeog-Soong (Department of Information and Communication Engineering, Kyungwoon University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.18, no.4, 2014 , pp. 825-832 More about this Journal
Abstract
Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require accurate detection of P-QRS-T point, higher computational cost and larger processing time. Even if some methods have the advantage in low complexity, but they generally suffer form low sensitivity. Also, it is difficult to detect PVC accurately because of the various QRS pattern by person's individual difference. Therefore it is necessary to design an efficient algorithm that classifies PVC based on QRS pattern in realtime and decreases computational cost by extracting minimal feature. In this paper, we propose PVC classification based on QRS pattern using QS interval and R wave amplitude. For this purpose, we detected R wave, RR interval, QRS pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through QS interval and R wave amplitude. The performance of R wave detection, PVC classification is evaluated by using 9 record of MIT-BIH arrhythmia database that included over 30 PVC. The achieved scores indicate the average of 99.02% in R wave detection and the rate of 93.72% in PVC classification.
Keywords
R wave; RR interval; QS interval; QRS pattern; PVC; R wave amplitude;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 G. Wubbeler, M. Stavridis, D. Kreiseler, R.-D. Bousseljot, and C. Elster, "Verification of humans using the electrocardiogram," Pattern Recognit.Lett., vol. 28, pp. 1172-1175, 2007.   DOI   ScienceOn
2 A. D. C. Chan, M. M. Hamdy, A. Badre, and V. Badee, "Wavelet distance measure for person identification using electrocardiograms," IEEE Trans. Instrum. Meas., vol. 57, no. 2, pp. 248-253, Feb. 2008.   DOI   ScienceOn
3 S. Chauhan, A. S. Arora, and A. Kaul, "A survey of emerging biometric modalites," Procedia Comput. Sci., vol. 2, pp. 213-218, 2010.   DOI
4 A. Teeramongkonrasmee, C. Tangwongsan and S. Sitthisook, "Development of a real-time cardiac arrhythmia analyzer," Proceedings of 32nd Electrical Engineering Conference (EECON-32), vol. 2, pp. 1367-1370, October, 2009.
5 Erik Zellmer, Fei Shang, Hao Zhang "Highly Accurate ECG Beat Classfication based on Continuous Wavelet Transformation and Multiple Support Vector Machine Classifiers," Biomedical Engineering and Informatics Conference MMEI, 2009, pp. 1-5, 2009.
6 Ince, T., Kiranyaz, S., Gabbouj, M, "Automated patientspecific classification of premature ventricular contractions," Proc. 30th Int. Conf. IEEE EMBS, 2008, pp. 5474-5477.
7 Shyu, L.Y., Wu, Y.H., Hu, W, "Using wavelet transform and fuzzy neural network for VPC detection from the holter ECG," IEEE Trans. Biomed. Eng., 2004, 51, (7), pp. 1269 -1273.   DOI   ScienceOn
8 Melgani, F., Bazi, Y, "Detecting premature ventricular contractions in ECG signals with Gaussian processes," Comput. Cardiol., 2008, 35, pp. 237-240.
9 Ik-Sung Cho et al., "Baseline Wander Removing Method Based on Morphological Filter for Efficient QRS Detection," Journal of KIICE, vol. 17, no. 1, 2013, pp.166-174.   과학기술학회마을   DOI   ScienceOn
10 Ik-Sung Cho, Hyeog-Soong Kwon, "Efficient QRS Detection and PVC Classification based on Profiling Method," Journal of KIICE, vol. 17, no. 4, 2013, pp.705-711.   과학기술학회마을   DOI
11 S. A. Israel, J. M. Irvine, A. Cheng, M. D. Wiederhold, and B. K. Wiederhold, "ECG to identify individuals," Pattern Recognit., vol. 38, no. 1, pp. 133-142, 2005.   DOI