The Transactions of The Korean Institute of Electrical Engineers (전기학회논문지)

The Korean Institute of Electrical Engineers (대한전기학회)
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Characterization of Premature Ventricular Contraction by K-Means Clustering Learning Algorithm with Mean-Reverting Heart Rate Variability Analysis

평균회귀 심박변이도의 K-평균 군집화 학습을 통한 심실조기수축 부정맥 신호의 특성분석

  • Received : 2017.06.08
  • Accepted : 2017.06.25
  • Published : 2017.07.01
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Abstract

Mean-reverting analysis refers to a way of estimating the underlining tendency after new data has evoked the variation in the equilibrium state. In this paper, we propose a new method to interpret the specular portraits of Premature Ventricular Contraction(PVC) arrhythmia by applying K-means unsupervised learning algorithm on electrocardiogram(ECG) data. Aiming at this purpose, we applied a mean-reverting model to analyse Heart Rate Variability(HRV) in terms of the modified poincare plot by considering PVC rhythm as the component of disrupting the homeostasis state. Based on our experimental tests on MIT-BIH ECG database, we can find the fact that the specular patterns portraited by K-means clustering on mean-reverting HRV data can be more clearly visible and the Euclidean metric can be used to identify the discrepancy between the normal sinus rhythm and PVC beats by the relative distance among cluster-centroids.

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References

  1. T. S. Beder, C. M. Marshall, Financial Engineering: the Evolution of a Profession, John Wiley & Sons, 2011.
  2. G. Montaveon, G. B. Orr, K. R. Muller, Nerual Networks: Tricks of the Trade, 2nd edition, Lecture Notes in Computer Science, 2012.
  3. U. R. Acharya, J. S. Suri, J. A. E. Spaan, S. M. Krishnan, Advances in Cardiac Signal Processing, Springer, 2007.
  4. R. Shouldice, C. Heneghan, P. Nolan, P. G. Nolan, "PR and PP ECG Intervals as Indicators of Autonomic Nervous Innervation of the Cardiac Sinoatrial and Atrioventricular Nodes," Proceedings of the 1st International IEEE EMBS, pp. 20-22, 2003.
  5. Jane Huff, ECG workout: Exercise in Arrhythmia Interpretation, 6th edition, Wolters Kluwer Health/Lippincott Williams & Wilkins, 2012.
  6. Gail Walraven, Basic Arrhythmia with 12-Lead EKGs, Upper Saddle River, NJ: Pearson Education, 2006.
  7. G. B. Moody, R. G. Mark, "The Impact of the MIT-BIH Arrhythmia Database," IEEE Eng. in Med. and Biol., Vol. 20, No. 3, pp. 45-50, 2001. https://doi.org/10.1109/51.932724
  8. F. F. Abushmmala, "Processing Overlapped Cells Using K-Means and Watershed," International Journal of Intelligent Information Systems, Vol. 3, No. 1, pp. 8-12, 2014. https://doi.org/10.11648/j.ijiis.20140301.12
  9. N. Dhanachandra, K. Manglem, Y. J. Chanu, "Image Segmentation Using K-Means Clustering Algorithm and Subtractive Clustering Algorithm," Procedia Computer Science, Vol. 54, pp. 764-771, 2015. https://doi.org/10.1016/j.procs.2015.06.090
  10. F. A. Shmmala, W. Ashour, "Color Based Image Segmentation Using Different Versions of K-Means in Two Spaces," Global Advanced Research Journal of Engineering, Technology and Innovation, Vol. 1, No. 9, pp. 30-41, January 2013.
  11. K. S. Kim, "Assessment of Premature Ventricular Contraction Arrhythmia by K-means Clustering Algorithm," Journal of the Korea Society of Computer and Information, Vol. 20, No. 5, pp. 65-72, 2017.
  12. J. M. Bae, An Illustrated Guide to Medical Statistics Using SPSS, Hannarae Publishing Co., 2012.
  13. G. Keller, Statistics for Management and Economics, 7th International Edition, International Thomson, 2005.