Plataforma computacional open-source e de arquitetura aberta para análise de sinais biomédicos
Resumo
Análises sobre a dinâmica dos sinais biomédicos são representações importantes do estado fisiológico de tecidos, orgãos, ou mesmo todo o corpo humano. Por isso, uma grande atenção é voltada para o estudo de métodos de análise que auxiliam na extração da maior quantidade de informações relevantes a partir destes dados. Este trabalho apresenta uma plataforma de software open-source e de arquitetura aberta para análise de sinais biomédicos, denominada JBioS. Implementada na linguagem Java, além de prover algumas facilidades para manipulação e pré-processamento dos dados, ela permite a implementação e integração fácil e rápida de novos métodos e funcionalidades computacionais através de plugins, promovendo o processo de validação de novos métodos de análise. Com essas características, a JBioS apresenta-se como uma ferramenta com potencial de aplicação tanto na pesquisa como no contexto clínico.Referências
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Malik, M., Bigger, J. T., Camm, A. J., Kleiger, R. E., Malliani, A., Moss, A. J., and Schwartz, P. J. (1996). Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. Circulation, 93(5):1043–1065.
May, R. M. (1976). Simple mathematical models with very complicated dynamics. Nature, 261:459–467.
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Richman, J. S. and Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol, 278(6):H2039–2049.
Shiavi, R. (2007). Random signal modeling and parametric spectral estimation. In Introduction to Applied Statistical Signal Analysis, pages 287 – 330. Academic Press, Burlington, third edition edition.
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So, H. and Chan, K. (1997). Development of qrs detection method for real-time ambulatory cardiac monitor. In Engineering in Medicine and Biology Society, 1997. Proceedings of the 19th Annual International Conference of the IEEE, volume 1, pages 289–292.
Theiler, J., Eubank, S., Longtin, A., Galdrikian, B., and Doyne Farmer, J. (1992). Testing for nonlinearity in time series: the method of surrogate data. Physica D Nonlinear Phenomena, 58:77–94.
Tsallis, C. (1988). Possible generalization of boltzmann-gibbs statistics. Journal of Statistical Physics, 52:479–487.
Umarov, S., Tsallis, C., and Steinberg, S. (2006). A generalization of the central limit theorem consistent with nonextensive statistical mechanics.
Clunie, D. A. (2009). PixelMed Java DICOM Toolkit. [link].
Costa, M., Goldberger, A. L., and Peng, C.-K. (2002). Multiscale entropy analysis of complex physiologic time series. Phys. Rev. Lett., 89(6):068102–.
Gilbert, D. and Morgner, T. (2010). Jfreechart project. [link].
Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., and Stanley, H. E. (2000). Physiobank, physiotoolkit, and physionet: Components of a new research resource for complex physiologic signals. Circulation, 101(23):e215–e220.
Grebogi, C., Ott, E., and Yorke, J. A. (1983). Crises, sudden changes in chaotic attractors, and transient chaos. Physica D Nonlinear Phenomena, 7:181–200.
Haykin, S. S. and Veen, B. V. (1998). Signals and Systems. John Wiley & Sons, Inc., New York, NY, USA.
Hyvärinen, A. (1999). Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks, 10(3):626–634.
Hénon, M. (1976). A two-dimensional mapping with a strange attractor. Communications in Mathematical Physics, 50:69–77.
Malik, M., Bigger, J. T., Camm, A. J., Kleiger, R. E., Malliani, A., Moss, A. J., and Schwartz, P. J. (1996). Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. Circulation, 93(5):1043–1065.
May, R. M. (1976). Simple mathematical models with very complicated dynamics. Nature, 261:459–467.
Pincus, S. M. (1991). Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences of the United States of America, 88(6):2297–2301.
Rasband, W. S. (1997-2010). ImageJ, U. S. National Institutes of Health, Bethesda, Maryland, USA. [link].
Richman, J. S. and Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol, 278(6):H2039–2049.
Shiavi, R. (2007). Random signal modeling and parametric spectral estimation. In Introduction to Applied Statistical Signal Analysis, pages 287 – 330. Academic Press, Burlington, third edition edition.
Silva, L. E. V. (2010). Ferramentas computacionais na análise da variabilidade da frequência cardíaca através do paradigma não extensivo no estudo de cardiopatias. Master’s thesis, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo.
So, H. and Chan, K. (1997). Development of qrs detection method for real-time ambulatory cardiac monitor. In Engineering in Medicine and Biology Society, 1997. Proceedings of the 19th Annual International Conference of the IEEE, volume 1, pages 289–292.
Theiler, J., Eubank, S., Longtin, A., Galdrikian, B., and Doyne Farmer, J. (1992). Testing for nonlinearity in time series: the method of surrogate data. Physica D Nonlinear Phenomena, 58:77–94.
Tsallis, C. (1988). Possible generalization of boltzmann-gibbs statistics. Journal of Statistical Physics, 52:479–487.
Umarov, S., Tsallis, C., and Steinberg, S. (2006). A generalization of the central limit theorem consistent with nonextensive statistical mechanics.
Publicado
20/07/2010
Como Citar
DUQUE, Juliano J.; SILVA, Luiz E. V.; MURTA JUNIOR, Luiz O..
Plataforma computacional open-source e de arquitetura aberta para análise de sinais biomédicos. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 10. , 2010, Belo Horizonte/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2010
.
p. 1590-1599.
ISSN 2763-8952.
