Proceedings of International Conference on Applied Innovation in IT  ·  2016/03/10  ·  Vol. 4  ·  Issue 1  ·  pp. 65–71
Similarity Measurement of Biological Signals Using Dynamic Time Warping Algorithm
Ivan Luzianin, Bernd Krause
The problem of similarity measurement of biological signals is considered on this article. The dynamic time warping algorithm is used as a possible solution. A short overview of this algorithm and its modifications are given. Testing procedure for different modifications of DTW, which are based on artificial test signals, are presented.
biological signal dynamic time warping ECG artificial signals testing methods.
References
  1. L. Sörnmo, P. Laguna, “Bioelectrical signal processing in cardiac and neurological applications, 1st Edition”, Academic Press, 2005.
  2. G. D. Clifford, F. Azuaje, P. E. McSharry, “Advanced Methods and Tools for ECG Data Analysis”, Norwood: Artech House Inc., 2006.
  3. C. Cassisi, P. Montalto et al. (2012). “Similarity Measures and Dimensionality Reduction Techniques for Time Series Data Mining”, in: A. Karahoca (Ed.), “Advances in Data Mining Knowledge Discovery and Applications”, InTech, 2012, pp. 71-94.
  4. Y. Jeong, M. K. Jeong, O. A. Omitaomu, “Weighted Dynamic Time Warping for Time Series Classification”, Pattern Recognition, No. 44, 2011, pp. 2231-2240.
  5. T.S. Han, S.K. Ko, J. Kang, “Efficient Subsequence Matching Using the Longest Common Subsequence with a Dual Match Index”, Proceeding MLDM of the 5th International Conference on Machine Learning and Data Mining in Pattern Recognition, Berlin Heidelberg: Springer, 2007, pp. 585-600.
  6. Y. Zhang, T. F. Edgar, “A Robust Dynamic Time Warping Algorithm for Batch Trajectory Synchronization”, American Control Conference, Seattle, June 2008, pp. 2864-2869.
  7. P. Tormene, T. Giorgio et al., “Matching incomplete time series with dynamic time warping: an algorithm and an application to post-stroke rehabilitation”, Artificial Intelligence in Medicine, No. 45, 2009, pp. 11-34.
  8. B. Huang, W. Kinsner, “ECG Frame Classification Using Dynamic Time Warping”, Proceedings of the IEEE Canadian conference on Electrical & Computer Engineering, 2002, pp. 1105-1110.
  9. L. R. Rabiner, A. E. Rosenberg and S. E. Levinson “Considerations in Dynamic Time Warping Algorithms for Discrete Word Recognition”, IEEE Transactions on Acoustics, Speech and Signal Processing, vol. ASSP-26, Dec. 1978, pp. 575-582.
  10. M. Müller, “Dynamic Time Warping”, Information Retrieval for Music and Motion, Berlin Heidelberg: Springer 2007, pp. 69-84.
  11. L. N. Sörnmo, M. E. Nygårds, “Deliniation of the QRS complex using the envelope of the e.c.g.”, Medical & Biological Engineering & Computing, No. 21, 1993, pp. 538-547.
  12. R. Atarius, L. Sörnmo, “Detection of Cardiac Late Potentials in nonstationary noise”, Med Eng Phys, 1997, pp. 291-298.
  13. P. Lander, E.J. Berbari “Time-Frequency Plane Wiener Filtering of the High-Resolution ECG: Background and Time-Frequency Representations”, IEEE Trans Biomed Eng., Apr. 1997, pp. 247-255.
  14. E.J. Keogh, M.J. Pazzani, “Derivative Dynamic Time warping”, Proceeding of the First SIAM of International Conference on Data Mining, 2007, pp. 1-11.
  15. T. Giorgino, “Computing and Visualizing Dynamic Time Warping Algorithms in R: The DTW Package”, Journal of Statistical Software, Vol. 31, Issue 7, 2009, pp. 1-25.
  16. M. Schmidt, M. Baumert et al., “Two-Dimensional Warping for One-Dimensional Signals — Conceptual Framework and Application to ECG Processing”, IEEE Transactions on Signal Processing, Vol. 62, No. 21, Nov. 2014, pp. 5577-5588.

Proceedings of the International Conference on Applied Innovations in IT by Anhalt University of Applied Sciences is licensed under CC BY-SA 4.0  ·  This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License

ICAIIT 2026
International Conference on Applied Innovation in IT
Navigation
Publisher
ISSN2199-8876
Location Anhalt University of Applied Sciences
Phone +49 (0) 3496 67 5611
Address Building 01, Room 425
Bernburger Str. 55
D-06366 Köthen, Germany
Open Access License

All works are licensed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0), unless otherwise noted.

Published by ICAIIT in cooperation with Anhalt University of Applied Sciences.

© 2026 ICAIIT — International Conference on Applied Innovations in IT. Anhalt University of Applied Sciences, Köthen, Germany.
Visitors: site traffic counter