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Related Documentation (Biomedical Toolkit)

LabVIEW 2013 Biomedical Toolkit Help

Edition Date: June 2013

Part Number: 373696B-01

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The following documents contain information that might be helpful as you use the LabVIEW Biomedical Toolkit.

The following list contains reference materials used to produce the Biomedical Toolkit. These references contain more information on the theory implemented in the Biomedical Toolkit.

Note  The following resources offer useful background information on the general concepts discussed in this documentation. These resources are provided for general informational purposes only and are not affiliated, sponsored, or endorsed by National Instruments. The content of these resources is not a representation of, may not correspond to, and does not imply current or future functionality in the Biomedical Toolkit or any other National Instruments product.
  • Bonato, P., T. D' Alessio, and M. Knaflitz. 1998. A statistical method for the measurement of muscle activation intervals from surface myoelectric signal during gait. IEEE Transactions on Biomedical Engineering, vol. 45, no. 3, pp. 287-299.
  • Brennan, M., M. Palaniswami, and P. Kamen. 2011. Do existing measures of Poincaré plot geometry reflect nonlinear features of heart rate variability. IEEE Transactions on Biomedical Engineering, 48(11), pp. 1342-1347.
  • Callaerts, D., J. Vandewalle, W. Sansen, J. Janssens, and G. Vantrappen. 1994. Acquisition and processing of the antepartum FECG. In A Critical Appraisal of Fetal Surveillance, edited by H. P. van Deijn and F. J. A. Copray. Amsterdam, The Netherlands: Elsevier Science B.V.
  • Clifford, G. D., and P. E. McSharry. 2004. A realistic coupled nonlinear artificial ECG, BP and respiratory signal generator for assessing noise performance of biomedical signal processing algorithms. Proceedings of the SPIE, vol 5467, pp. 290-301, 2004.
  • Clifford, G. D., F. Azuaje, and P. E. McSharry. 2006. Advanced Methods and Tools for ECG Data Analysis. Norwood, MA: Artech House Publishers.
  • Foster, F. K., and D. Turney. 1986. Oscillometric determination of diastolic, mean and systolic blood pressure – a numerical model. Journal of Biomedical Engineering, vol. 108, no. 11, pp. 359-364.
  • Geddes, L. A., M. Voelz, C. Combs, D. Reiner, and C. F. Babbs. 1982. Characterization of the oscillometric method for measuring indirect blood pressure. Annals of Biomedical Engineering, vol. 10, pp. 271-280.
  • Goldberger, A. L., L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. Ch. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C. K. Peng, H. E. Stanley. 2000. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation, 101(23):e215-e220 (June 13). PMID: 10851218; doi: 10.1161/01.CIR.101.23.e215.
  • Laguna, P., R. Jané, and P. Caminal. 1994. Automatic detection of wave boundaries in multilead ECG signals: validation with the CSE database. Journal of Computers and Biomedical Research, vol. 27, Issue. 1, February.
  • Lorensen, W. E., and H. E. Cline. 1987. Marching cubes: a high resolution 3D surface construction algorithm. Computer Graphics, volume 21 (July), Number 4.
  • Malik et al. 1996. Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. Circulation, 93: 1043-1065.
  • Mcsharry, P. E., G. D. Clifford, L. Tarassenko, and L. A. Smith. 2003. A dynamic model for generating synthetic electrocardiogram signals. IEEE Transactions on Biomedical Engineering, vol. 50, no. 3, pp. 289-294.
  • Merletti, R., and P. A. Parker. 2004. Electromyography – Physiology, Engineering, and Noninvasive Applications. NJ: John Wiley & Sons.
  • Pan, J., and W. J. Tompkins. 1985. A real-time QRS detection algorithm. IEEE Transactions on Biomedical Engineering, vol. 32: 230-236.
  • Peng, C. K., S. Havlin, H. E. Stanley, and A. L. Goldberger. 1995. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos, 5 (1),pp. 82-87.
  • Shwedyk, E., R. Balasubramanian, and R. N. Scott. 1977. A nonstationary model for the electromyogram. IEEE Transactions on Biomedical Engineering, vol. BME-24, no. 5, pp. 417-424.
  • Thakor, N. V., and Y. S. Zhu. 1991. Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection. IEEE Transactions on Biomedical Engineering, vol. 38: 785-794.
  • Tompkins, W. J. 1993. Biomedical digital signal processing: C-language examples and laboratory experiments for the IBM PC. NJ: Prentice Hall.
  • Zetterberg, L. H., and K. Ahlin. 1975. Analogue simulator of e.e.g. signals based on spectral components. Medical and Biological Engineering and Computing, vol. 13, Number 2, 272-278.

 

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