Time Series Clustering Algorithm for Two-Modes Cyclic Biosignals

  • Neuza Nunes
  • Tiago Araújo
  • Hugo Gamboa
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 273)


In this study, an automatic algorithm which computes a meanwave is introduced. The meanwave is produced by averaging all cycles of a cyclic signal, sample by sample. With that information, the signal’s morphology is captured and the similarity among its cycles is measured. A k-means clustering procedure is used to distinguish different modes in a cyclic signal, using the distance metric computed with the meanwave information. The algorithm produced is signal-independent, and therefore can be applied to any cyclic signal with no major changes in the fundamental frequency. To test the effectiveness of the proposed method, we’ve acquired several biosignals in context tasks performed by the subjects with two distinct modes in each. The algorithm successfully separates the two modes with 99.3% of efficiency. The fact that this approach doesn’t require any prior information and its preliminary good performance makes it a powerful tool for biosignals analysis and classification.


Biosignals Waves Meanwave k-Means Clustering Algorithm Signal-processing 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Theis, F., Meyer-Base, A.: Biomedical Signal Analysis: Contemporary Methods and Applications. The MIT Press (2010)Google Scholar
  2. 2.
    Ben-Arie, J., Wang, Z., Pandit, P., Rajaram, S.: Human Activity Recognition Using Multidimensional Indexing. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(8), 1091–1104 (2002)CrossRefGoogle Scholar
  3. 3.
    Baek, J., Lee, G., Park, W., Yun, B.: Accelerometer Signal Processing for User Activity Detection. Knowledge-Based Intelligent Information & Engineering Systems 3215, 610–617 (2004)CrossRefGoogle Scholar
  4. 4.
    Smith, J., Fishkin, K., Jiang, B., Mamishev, A., Philipose, M., Rea, A., Roy, S., Sundara-Rajan, K.: RFID-Based Techniques for Human-Activity Detection. Communications of the ACM 48(9), 39–44 (2005)CrossRefGoogle Scholar
  5. 5.
    Fridlund, A., Schwartz, G., Fowler, S.: Pattern Recognition of Self-Reported Emotional State from Multiple-Site Facial EMG Activity During Affective Imagery. Society for Psychophysiological Research 21(6), 622–637 (2007)Google Scholar
  6. 6.
    Basseville, M., Nikiforov, I.: Detection of Abrupt Changes: Theory and Applications. Prentice-Hall Inc. (1993)Google Scholar
  7. 7.
    Ghahramani, Z.: Unsupervised learning. In: Bousquet, O., von Luxburg, U., Rätsch, G. (eds.) Machine Learning 2003. LNCS (LNAI), vol. 3176, pp. 72–112. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  8. 8.
    Wu, X., Kumar, V., Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G., Ng, A., Liu, B., Yu, P., Zhou, Z., Steinbach, M., Hand, D., Steinberg, D.: Top 10 algorithms in data mining. Knowledge Information Systems 14(1), 1–37 (2007)CrossRefGoogle Scholar
  9. 9.
    Liao, W.: Clustering of time series data – a survey. Pattern Recognition 38, 1857–1874 (2005)zbMATHCrossRefGoogle Scholar
  10. 10.
    Quiroga, R.: Spike sorting. Scholarpedia 2(12), 3583 (2007)CrossRefGoogle Scholar
  11. 11.
    PLUX – Wireless Biosignals, bioPLUX Research Manual - internal report (2010)Google Scholar
  12. 12.
    Andersson, E., Supej, M., Sandbakk, Ø., Sperlich, B., Stöggl, T., Holmberg, H.: Analysis of sprint cross-country skiing using a differential global navigation satellite system. European Journal of Applied Physiology 110(3), 585–595 (2010)CrossRefGoogle Scholar
  13. 13.
    Myklebust, H., Nunes, N., Hallén, J., Gamboa, H.: Morphological analysis of acceleration signals in cross-country skiing - information extraction and technique transitions detection. In: Procedings of the 4th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2011), Rome, Italy (2011)Google Scholar
  14. 14.
  15. 15.
    Oliphant, T.: Guide to Numpy. Tregol Publishing (2006)Google Scholar
  16. 16.
    Oliphant, T.: SciPy Tutorial,
  17. 17.
    Martins, D., Mattos, M., Simões, P., Cechinel, C., Bettiol, J., Barbosa, A.: Aplicação do Algoritmo K-Means em Dados de Prevalência da Asma e Rinite em Escolares. In: XI Congresso Brasileiro de Informática em Saúde (2008)Google Scholar
  18. 18.
    Gerhard, D.: Pitch extraction and fundamental frequency: History and current techniques. Technical Report (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Neuza Nunes
    • 1
  • Tiago Araújo
    • 1
    • 2
  • Hugo Gamboa
    • 1
    • 2
  1. 1.Physics DepartmentFCT-UNLCaparicaPortugal
  2. 2.PLUX – Wireless BiosignalsLisbonPortugal

Personalised recommendations