RECOVERING VITAL PHYSIOLOGICAL SIGNALS FROM AMBULATORY DEVICES

Praveen Pankajakshan, Rangavittal Narayanan

References

  1. [1] J. Han, M. Kamber, and J. Pei, Data Mining: Con-cepts and Techniques, 3rd ed., ser. The Morgan Kauf-mann Series in Data Management Systems, J. Gray,Ed. Morgan-Kaufmann, 2011.
  2. [2] G. B. Moody and R. G. Mark, “The impact of the MIT-BIH Arrhythmia Database,” IEEE Eng. Med. Biol.Mag., vol. 20, no. 3, pp. 45–50, 2001.
  3. [3] A. L. Goldberger, L. A. N. Amaral, L. Glass,J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E.Mietus, G. B. Moody, C.-K. Peng, and H. E.Stanley, “PhysioBank, PhysioToolkit, and PhysioNet:Components of a New Research Resource for ComplexPhysiologic Signals,” Circulation, vol. 101, no. 23,pp. e215–e220, 2000. [Online]. Available: http://circ.ahajournals.org/cgi/content/full/101/23/e215
  4. [4] J. Pan and W. J. Tompkins, “A real-time QRS detectionalgorithm,” IEEE Trans. Biomedical Engg., vol. BME-32, no. 3, pp. 230–236, March 1985.
  5. [5] B.-U. K¨ohler, C. Hennig, and R. Orglmeister, “ThePrinciples of Software QRS Detection,” IEEE Eng.Med. Biol. Mag., vol. 21, no. 1, pp. 42–57, 2002.
  6. [6] A. N. Tikhonov and V. Y. Arsenin, Solutions of ill-posed problems, F. John, Ed. Wiley, New York, 1977.
  7. [7] L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear totalvariation based noise removal algorithms,” Physica D:Nonlinear Phenomena, vol. 60, pp. 259–268, 1992.
  8. [8] M. Figueiredo, J. Bioucas-Dias, J. P. Oliveira, andR. D. Nowak, “On total-variation denoising: A newmajorization-minimization algorithm and an experi-mental comparison with wavalet denoising,” in Proc.IEEE Int. Conf. Image Processing, 2006.
  9. [9] S.-J. Kim, K. Koh, S. Boyd, and D. Gorinevsky, “ 1Trend Filtering,” SIAM Review, vol. 51, no. 2, pp. 339–360, 2009.

Important Links:

Go Back