Study of Motif Size Parameter for ECG Time Series Classification
Abstract
Currently, there is a growing interest in different areas for the analysis of data that present temporal dependency. In the medical area, information of this kind is daily recorded, however, only a small portion is analyzed due to lack of methods and tools. In this sense, the identification of morphological patterns (motifs) in time series is an important tool for analyzing such data. In this work, we present an initial study about the influence of the parameter size of the motif considering the classification task, applied to medical temporal databases, such as the electrocardiogram tests. The results show a significant influence of the motifís size in the classification process.References
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Buhler, J. and Tompa, M. (2002). Finding motifs using random projections. Journal of Computational Biology, 9(2):225–242.
Chiu, B., Keogh, E., and Lonardi, S. (2003). Probabilistic discovery of time series motifs. In Proceedings of the 9th International Conference on Knowledge Discovery and Data Mining, pages 493–498, New York, USA.
Hilbert, M. and López, P. (2011). The world’s technological capacity to store, communicate, and compute information. Sciene Magazine.
Jovic, A. and Bogunovic, N. (2010). Electrocardiogram analysis using a combination of statistical, geometric, and nonlinear heart rate variability features. Artificial Intelligence in Medicine, In Press, Corrected Proof.
Keogh, E. and Kasetty, S. (2002). On the need for time series data mining benchmarks: a survey and empirical demonstration. In Proceedings of the 8th International Conference on Knowledge Discovery and Data Mining, pages 102–110, New York, USA.
Lin, J., Keogh, E., Lonardi, S., and Patel, P. (2002). Finding motifs in time series. In Proceedings of the Second Workshop on Temporal Data Mining at the Eighth International Conference on Knowledge Discovery and Data Mining, pages 53–68, Canada.
Maletzke, A. G. (2009). Uma metodologia para extração de conhecimento em séries temporais por meio da identificação de motifs e da extração de características. Dissertação de mestrado, Universidade de São Paulo, São Carlos, São Paulo, Brasil.
Maletzke, A. G. and Batista, G. E. (2010). Mineração de dados temporais mediante a identificação de motifs e a extração de características. In Anais do VII Best MSc Dissertation/PhD Thesis Contest - Joint Conference 2010, volume 1, pages 1–12, São Bernardo do Campo - SP.
Maletzke, A. G., Batista, G. E., and Lee, H. D. (2008). Uma avaliação sobre a identificação de motifs em séries temporais. In Anais do Congresso da Academia Trinacional de Ciências, volume 1, pages 1–10, Foz do Iguaçu, Brasil.
Mar, T., Zaunseder, S., Cortes, J. P. M., Soria, M. L., and Poll, R. (2011). Optimization of ecg classification by means of feature selection. Biomedical Engineering, (99):1.
Ministério da Saúde (2009). Elsa brasil: maior estudo epidemiológico da américa latina. Revista Saúde Pública, 43(1).
Mitchell, T. M. (1997). Machine Learning. McGraw-Hill, Boston, USA.
Monard, M. C. and Baranauskas, J. A. (2003). Sistemas Inteligentes: fundamentos e aplicações, chapter Conceitos sobre Aprendizado de Máquina, pages 89–114. Editora Manole, Barueri, Brasil.
Motulsky, H. (1995). Intuitive Biostatistics. Oxford University Press, New York, USA.
Neagoe, V.-E., Iatan, I.-F., and Grunwald, S. (2003). A neuro-fuzzy approach to classification of ecg signals for ischemic heart disease diagnosis. In Proceedings of the Annual Symposium Proceedings Archive at the Americam Medical Informatics Association, pages 494–498.
Olszewski, R. T. (2001). Generalized Feature Extraction for Structural Pattern Recognition in Time-Series Data. PhD thesis, Carnegie Mellon University, Pittsburgh, PA.
Osowski, S., Siwek, K., and Siroic, R. (2011). Neural system for heartbeats recognition using genetically integrated ensemble of classifiers. Computers in Biology and Medicine, 41(3):173 – 180.
Thanapatay, D., Suwansaroj, C., and Thanawattano, C. (2010). Ecg beat classification method for ecg printout with principle components analysis and support vector machines. In Proceedings of The International Conference on Electronics and Information Engineering, pages 72–75.
Yu, S.-N. and Chen, Y.-H. (2007). Electrocardiogram beat classification based on wavelet transformation and probabilistic neural network. Pattern Recognition Letters, 28:1142–1150.
Published
2011-07-19
How to Cite
MALETZKE, André Gustavo; LEE, Huei Diana; ZALEWSKI, Willian; OLIVA, Jefferson Tales; MACHADO, Renato Bobsin; COY, Cláudio Saddy Rodrigues; FAGUNDES, João José; WU, Feng Chung.
Study of Motif Size Parameter for ECG Time Series Classification. In: BRAZILIAN SYMPOSIUM ON COMPUTING APPLIED TO HEALTH (SBCAS), 11. , 2011, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2011
.
p. 1806-1815.
ISSN 2763-8952.
