Research on Pattern Recognition Applied for Volume Estimation of Blood Chamber with Matrix of Optical Sensors

• Authors: P. Marczyński , G. Konieczny , T. Pustelny
• Languages of publication: EN

Abstracts

EN

The following article describes research on possibility of using pattern recognition algorithms in the optical measurement system for estimation of the blood chamber volume in the Polish Ventricular Assist Device (POLVAD). The optical system is being developed at the Department of Optoelectronics, Silesian University of Technology, Poland. Data analysis methods include a feature subset selection algorithm involving principal components analysis and objective function as quality criterion. The analysis takes into account 17 patterns reflecting particular volumes. The k-nearest neighbours method is used as pattern classifier. The pattern recognition system was initially designed for an array of gas sensors and this paper describes its further development.

Keywords

EN

42.30.Sy; 47.54.-r; 43.60.Lq

Discipline

• 47.54.-r: Pattern selection; pattern formation(see also 82.40.Ck Pattern formation in reactions with diffusion, flow and heat transfer in Physical chemistry and chemical physics; 87.18.Hf Spatiotemporal pattern formation in cellular populations in Biological and medical physics)
• 42.30.Sy: Pattern recognition

• Publisher: Institute of Physics, Polish Academy of Sciences
• Journal: Acta Physica Polonica A
• Year: 2013
• Volume: 124
• Issue: 3
• Pages: 498-501

Dates

published

2013-09

Contributors

author

P. Marczyński

• Department of Optoelectronics, Silesian University of Technology, Akademicka 2, 44-100 Gliwice, Poland

author

G. Konieczny

• Department of Optoelectronics, Silesian University of Technology, Akademicka 2, 44-100 Gliwice, Poland

author

T. Pustelny

• Department of Optoelectronics, Silesian University of Technology, Akademicka 2, 44-100 Gliwice, Poland

References

• [1] R. Gutierrez-Osuna, T. Nagle, IEEE Trans. Syst., Man Cybernet. Part B, Cybernet. 29, 626 (1999)
• [2] A.H. Gómez, J. Wang, G. Hu, A.G. Pereira, Sensors Actuat. B 113, 347 (2006)
• [3] Y. Yin, X. Tian, Sensors Actuat. B 124, 393 (2007)
• [4] K. Gut, Bull. Pol. Acad. Sci., Techn. Sci. 59, 395 (2011)
• [5] K.Z. Mao, IEEE Trans. Syst., Man Cybernet. Part B, Cybernet. 34, 629 (2004)
• [6] P. Marczyński, A. Szpakowski, C. Tyszkiewicz, T. Pustelny, Acta Phys. Pol. A 122, 847 (2012)
• [7] G. Konieczny, T. Pustelny, Acta Phys. Pol. A 122, 962 (2012)
• [8] M. Scholz, R. Vigário, Artificial Neural Networks, Springer, Brugges 2002

Identifiers

DOI

10.12693/APhysPolA.124.498