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Signal processing approach for breath prediction pattern recognition

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper, a new approach of signal processing for breath prediction pattern recognition is proposed and further analyses are presented. In order to extract key values from raw data, a shift from time domain to phase space has been utilized. It helped to achieve clearer peak-to-peak measurements which are crucial for breath prediction pattern recognition. Based on a special software tool for breath prediction pattern recognition several different algorithms have been compared. As a result, a reduction in error rate can be achieved when applying a new signal processing approach in comparison to the previous designs.
Rocznik
Strony
51--60
Opis fizyczny
Bibliogr. 11 poz., rys., tab.
Twórcy
  • Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, Szczecin, Poland
autor
  • Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, Szczecin, Poland
Bibliografia
  • [1] Chen, Z., Brown, E. N., Barbieri, R.: Assessment of Autonomic Control and Respiratory Sinus Arrhythmia Using Point Process Models of Human Heart Beat Dynamics. IEEE Transactions on Electromagnetic Compatibility, 56(7), July 2009.
  • [2] Ruan, D., Fessler, J. A., Balter, J. M.: Real-time prediction of respiratory motion based on local regression methods. Phys. Med. Biol. 52(23), pp. 7137–7152, 2007.
  • [3] Sharp, G. C., Jiang, S. B., Shimizu, S., Shirato, H.: Prediction of respiratory tumor motion for real-time image-guided radiotherapy. Phys. Med. Biol. 49, 2004, pp. 425–440.
  • [4] Batzel, J. J., Novak, V., Kappel, F., Olufsen, M. S., Tran, H. T.: Introduction to the special issues: Short-term cardiovascular-respiratory control mechanisms. Cardiovasc. Eng., 8(1), pp. 1–4, 2008.
  • [5] Hirsch, J. A., Bishop, B.: Respiratory sinus arrhythmia in humans: How breathing pattern modulates heart rate. Amer. J. Physiol., 241(4), pp. H620–H629, 1981.
  • [6] Saul, J. P., Berger, R. D., Chen, M. H., Cohen, R. J.: Transfer function analysis of autonomic regulation. II. Respiratory sinus arrhythmia. Amer. J. Physiol. Heart Cicr. Physiol., 256(25), pp. 153–161, 1989.
  • [7] Pinna, G. D., Maestri, R., La Rovere, M. T., Gobbi, E., Fanfulla, F.: Effect of paced breathing on ventilatory and cardiovascular variability parameters during short-term investigations of autonomic function. Amer. J. Physiol. Heart Cicr. Physiol., 290(1), pp. 424–433, 2006.
  • [8] Lippmann, R. P.: An introduction to computing with neural nets. IEEE Acoustical Speech and Signal Processing Magazine, 3(4), pp. 4–22, 1987.
  • [9] Murphy, M. J., Dieterich, S.: Comparative performance of linear and nonlinear neural networks to predict irregular breathing. Phys. Med. Biol. 51, pp. 5903–5914, 2006.
  • [10] Ruan, D., Fessler, J. A., Balter, J. M., Sonke, J.-J.: Exploring breathing pattern irregularity with projection-based method. Med. Phys. 33(7), pp. 2491–2499, 2006.
  • [11] Bishop, C.: Neural Networks for Pattern Recognition. Oxford University Press, 1995.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2a6b5f6e-9ef1-408f-9289-b0be202a94bd
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