Computer guided analysis of hemodialyzed patients’ bioimpedance spectroscopy parameters

Authors

  • Teresa Małecka-Massalska Physiology Department, Medical University of Lublin / Nephrology Department, Medical University of Lublin Author
  • Ryszard Maciejewski Human Anatomy Department, Medical University of Lublin Author
  • Krystyna Lupa Physiology Department, Medical University of Lublin Author
  • Piotr Wąsiewicz Institute of Electronic Systems, Warsaw University of Technology Author
  • Wojciech Załuska Nephrology Department, Medical University of Lublin Author
  • Andrzej Książek Nephrology Department, Medical University of Lublin Author

Keywords:

data retrieval, decision tree, hydrate status, extracellular compartment, intracellular compartment

Abstract

Introduction. Monitoring the hydration level in dialyzed patients is an important clinical aspect of treatment quality. Bioimpedance is one of the methods using electric properties of body tissues subjected to an alternating multi-frequency amplitude current in order to assess hydration states. 

Aim. The aim of the study was to use the computer guided analysis of hemodialyzed patients’ bioimpedance spectroscopy parameters for desion trees algorithm. 

Material and methods. The measurements were conduc-ted on two groups – 50 patients on chronic hemodialysis (the test group) 10 minutes before hemodialysis and 46 healthy volunteers (the control group). The studied parameters were: TBW (total body water), ECW (extracellular water) and ICW (intracellular water). For bioimpedance measurements a bio-impedance analyzer was used (Xitron Technologies, San Diego, CA, USA, model 4000B Bioimpedance spectroscopy device measuring at 50 frequencies between 5 kHz and 1 MHz) with electrodes (7.7 x 1.9 cm²). Bioimpedance was measured in a logarithmic spectrum of 10 frequencies starting from 5 to 500 kHz. 

Results. The results of calculations on normal, discredited and normalized data were computed in the R language environment with algorithms from RWeka library to generate simple rules of identification of the aforementioned diseases. The executed experiments affirm a possibility to create good classifiers for detecting a proper patient with the help of J48 (decision tree) i QDA (quadratic discriminant analysis) but only after previous training. Thanks to an appropriate algorithm for testing classifiers, decision trees with the classification error below 5 % were obtained. In the future it will be possible to further diminish the classification error by measuring more variables using more complex algorithms for testing classifiers.

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Published

2010-12-01