Lung model parameter estimation by unscented Kalman filter

SAATÇI E., Akan A.

29th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society, Lyon, Fransa, 22 - 26 Ağustos 2007, ss.2556-2557 identifier identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/iembs.2007.4352850
  • Basıldığı Şehir: Lyon
  • Basıldığı Ülke: Fransa
  • Sayfa Sayıları: ss.2556-2557
  • İstanbul Kültür Üniversitesi Adresli: Evet

Özet

Dynamic nonlinear models are the best choice to analyze respiratory systems and to describe system mechanics. In this work, Unscented Kalman Filtering (UKF) was used to estimate the dynamic nonlinear model parameters of the lung model by using the measured airway flow, mask pressure and integrated lung volume. Artificially generated data and the data from Chronic Obstructive Pulmonary Diseased (COPD) patients were analyzed by the proposed model and the proposed UKF algorithm. Simulation results for both cases demonstrated that UKF is a promising estimation method for the respiratory system analysis.