Advanced SearchSearch Tips
Premature Contraction Arrhythmia Classification through ECG Pattern Analysis and Template Threshold
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Premature Contraction Arrhythmia Classification through ECG Pattern Analysis and Template Threshold
Cho, Ik-sung; Cho, Young-Chang; Kwon, Hyeog-soong;
  PDF(new window)
Most methods for detecting arrhythmia require pp interval, diversity of P wave morphology, but it is difficult to detect the p wave signal because of various noise types. Therefore it is necessary to use noise-free R wave. In this paper, we propose algorithm for premature contraction arrhythmia classification through ECG pattern analysis and template threshold. For this purpose, we detected R wave through the preprocessing method using morphological filter, subtractive operation method. Also, we developed algorithm to classify premature contraction wave pattern using weighted average, premature ventricular contraction(PVC) and atrial premature contraction(APC) through template threshold for R wave amplitude. The performance of R wave detection, PVC classification is evaluated by using 6 record of MIT-BIH arrhythmia database that included over 30 PVC and APC. The achieved scores indicate the average of 99.77% in R wave detection and the rate of 94.91%, 95.76% in PVC and APC classification.
ECG pattern;template threshold;RR interval;R wave amplitude;PVC;APC;
 Cited by
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. crossref(new window)

S. Chauhan, A. S. Arora, and A. Kaul, "A survey of emerging biometric modalites," Procedia Comput. Sci., vol. 2, pp. 213-218, 2010. crossref(new window)

J. W. Schleifer and K. Srivathsan, "Ventricular arrhythmias: State of the art," Cardiol. Clin., vol. 31, no. 4, pp. 595-605, 2013. crossref(new window)

S.-Y. Lee, J.-H. Hong, C.-H. Hsieh, M.-C. Liang, S.-Y. C. Chien, and K.-H. Lin, "Low-power wireless ECG acquisition and classification system for body sensor networks," IEEE J. Biomed. Health Informat., vol. 19, no. 1, pp. 236-246, Jan. 2015. crossref(new window)

Y.-P. Chen et al., "An injectable 64 nW ECG mixed-signal SoC in 65 nm for arrhythmia monitoring," IEEE J. Solid-State Circuits, vol. 50, no. 1, pp. 375-390, Jan. 2015. crossref(new window)

A. Amann, R. Tratnig, and K. Unterkofler, "Detecting ventricular fibrillation by time-delay methods," IEEE Trans. Biomed. Eng., vol. 54, no. 1, pp. 174-177, Jan. 2007. crossref(new window)

Thong, T., J. McNames, M. Aboy, B. Goldstein. Prediction of paroxysmal atrial fibrillation by analysis of atrial premature complexes. IEEE Trans. On Biomed. Eng., vol. 51, no. 4, pp. 561-569, April. 2004. crossref(new window)

J. Pan, W.J. Tompkins, A real-time QRS detection algorithm, IEEE Trans. Biomed. Eng. BME vol. 32, no. 3, pp.230-236, March. 1985.

G. Yang, J. Chen, Y. Cao, H. Tenhunen, and L. Zheng, "A novel wearable ECG monitoring system based on activecable and intelligent electrodes," in 10th International Conference on e-health Networking, Applications and Services, 7-9. pp. 156-159, 2008.

K. N. Plataniotis, D. Hatzinakos, and J. K. M. Lee, "ECG biometric recognition without fiducial detection," in Proc. Biometrics Symp./Biometrics Consortium Conf., Baltimore, MD, pp. 1-6, 2006.

Zhang, F. & Lian, Y, "QRS detection based on multi-scale mathematical morphology for wearable ECG devices in body area networks," IEEE Transaction on Biomedical Circuits and Systems, vol. 3, no. 4, pp.220-228, Jun. 2009. crossref(new window)

F. Agrafioti and D. Hatzinakos, "ECG biometric analysis in cardiac irregularity conditions," Signal, Image Video Process., vol. 3, pp. 329-343, 2009. crossref(new window)