Time Series Clustering Algorithm for Two-Modes Cyclic Biosignals
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.
KeywordsBiosignals Waves Meanwave k-Means Clustering Algorithm Signal-processing
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- 1.Theis, F., Meyer-Base, A.: Biomedical Signal Analysis: Contemporary Methods and Applications. The MIT Press (2010)Google Scholar
- 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.Basseville, M., Nikiforov, I.: Detection of Abrupt Changes: Theory and Applications. Prentice-Hall Inc. (1993)Google Scholar
- 11.PLUX – Wireless Biosignals, bioPLUX Research Manual - internal report (2010)Google Scholar
- 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.OpenSignals, http://www.opensignals.net
- 15.Oliphant, T.: Guide to Numpy. Tregol Publishing (2006)Google Scholar
- 16.Oliphant, T.: SciPy Tutorial, http://www.scipy.org/
- 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.Gerhard, D.: Pitch extraction and fundamental frequency: History and current techniques. Technical Report (2003)Google Scholar