Open-source computational platform and open architecture for biomedical signal analysis
Abstract
Dynamical analysis upon biomedical signals are important representations of the physiological state in tissues, organs, or even entire human body. Therefore, much attention is devoted to the study of analysis methods that helps to extract the largest amount of relevant information from these data. This paper presents an open-source and open-architecture software platform for biomedical signal analysis, called JBioS. Implemented in Java, in addition to providing resources for data handling and pre-processing, it easily allows a rapid implementation and integration of new computational functionalities or methods through plugins, promoting validation process of new analysis methods. With these features, JBioS presents itself as a tool with potential applications in both research and clinical settings.References
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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.
Published
2010-07-20
How to Cite
DUQUE, Juliano J.; SILVA, Luiz E. V.; MURTA JUNIOR, Luiz O..
Open-source computational platform and open architecture for biomedical signal analysis. In: BRAZILIAN SYMPOSIUM ON COMPUTING APPLIED TO HEALTH (SBCAS), 10. , 2010, Belo Horizonte/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2010
.
p. 1590-1599.
ISSN 2763-8952.
