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Polish Information Processing Society
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Annals of Computer Science and Information Systems, Volume 11

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

The North Sea Bicycle Race ECG Project: Time-Domain Analysis

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DOI: http://dx.doi.org/10.15439/2017F303

Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 1353–1356 ()

Full text

Abstract. Analysis of electrocardiogram and heart rate provides useful information about health condition of a patient. The North Sea Bicycle Race is an annual competition in Norway. Examination of ECG recordings collected from participants of this race may allow defining and evaluating the relationship between physical endurance exercises and heart electrophysiology. Parameters reflecting potentially alarming deviations in the latter are to be identified in this study. This paper presents results of a time-domain analysis of ECG data collected in 2014, implementing K-Means clustering. A double stage analysis strategy, aimed at producing hierarchical clusters, is proposed. The first phase allows rough separation of data. Second stage reveals internal structure of the majority clusters. In both steps, discrepancies driving the separation could stem from three sources. The clusters were defined predominantly by combinations of features: heartbeat signals correlation, P-wave shape, and RR intervals; none of the features alone was discriminative for all the clusters.

References

  1. X. Dong, C. Wang, and W. Si, “ECG beat classification via deterministic learning,” Neurocomputing, vol. 240, pp. 1–12, May 2017.
  2. F. Castells, P. Laguna, L. Sornmo, A. Bollmann, and J. Roig, “Principal component analysis in ECG signal processing,” Eurasip Journal on Advances in Signal Processing, 2007.
  3. A. Daamouche, L. Hamami, N. Alajlan, and F. Melgani, “A wavelet optimization approach for ECG signal classification,” Biomedical Signal Processing and Control, vol. 7, pp. 342–349, July 2012.
  4. D. Benitez, P. Gaydecki, A. Zaidi, and A. Fitzpatrick, “The use of the Hilbert transform in ECG signal analysis,” Computers in Biology and Medicine, vol. 31, no. 5, pp. 399–406, 2001. 399.
  5. M. Moavenian and H. Khorrami, “A qualitative comparison of Artificial Neural Networks and Support Vector Machines in ECG arrhythmias classification,” Expert Systems With Applications, vol. 37, pp. 3088–3093, Apr. 2010.
  6. M. A. Rahhal, Y. Bazi, H. AlHichri, N. Alajlan, F. Melgani, and R. Yager, “Deep learning approach for active classification of electro-cardiogram signals,” Information Sciences, vol. 345, pp. 340–354, June 2016.
  7. M. Lagerholm, C. Peterson, G. Braccini, L. Edenbrandt, and L. Sornmo, “Clustering ECG complexes using Hermite functions and self-organizing maps,” IEEE Transactions on Biomedical Engineering, vol. 47, pp. 838–848, July 2000.
  8. A. Lourenco, H. Silva, P. Leite, R. Lourenco, and A. Fred, “Real Time Electrocardiogram Segmentation for Finger based ECG Biometrics (PDF) - Semantic Scholar.”
  9. I. I. Christov, “Real time electrocardiogram QRS detection using combined adaptive threshold,” BioMedical Engineering OnLine, vol. 3, p. 28, 2004.
  10. A. Gautam, Y. D. Lee, and W. Y. Chung, “ECG Signal De-noising with Signal Averaging and Filtering Algorithm,” in 2008 Third International Conference on Convergence and Hybrid Information Technology, vol. 1, pp. 409–415, Nov. 2008.
  11. P. Laguna, R. Jane, and P. Caminal, “Automatic detection of wave boundaries in multilead ECG signals: Validation with the CSE database,” Computers and biomedical research, vol. 27, no. 1, pp. 45–60, 1994.
  12. P. W. Macfarlane, B. Devine, and E. Clark, “The university of Glasgow (Uni-G) ECG analysis program,” in Computers in Cardiology, 2005, (Lyon), pp. 451–454, Sept. 2005.
  13. “Glasgow 12-lead Analysis Program - Physician’s Guide.”
  14. K. Wang, R. W. Asinger, and H. J. Marriott, “ST-segment elevation in conditions other than acute myocardial infarction,” New England Journal of Medicine, vol. 349, no. 22, pp. 2128–2135, 2003.
  15. U. Demsar, P. Harris, C. Brunsdon, A. S. Fotheringham, and S. McLoone, “Principal Component Analysis on Spatial Data: An Overview,” Annals of the Association of American Geographers, vol. 103, pp. 106–128, Jan. 2013.
  16. B. Hariharan, J. Malik, and D. Ramanan, “Discriminative Decorrelation for Clustering and Classification,” in Computer Vision - ECCV 2012, pp. 459–472, Springer, Berlin, Heidelberg, Oct. 2012.
  17. P. J. Rousseeuw, “Silhouettes: a graphical aid to the interpretation and validation of cluster analysis,” Journal of computational and applied mathematics, vol. 20, pp. 53–65, 1987.