Akciğer Mekaniğinin Elektriksel Modelinin Çıkarılması ve Basınç Dalga Şeklinin Elde Edilmesi

Bu çalışmada, insanların yaşamlarını sürdürmesi için önemli bir organ olan akciğerin elektriksel modelininçıkarılması işlemi gerçekleştirilmiştir. Çalışmamızın amacı akciğerin elektriksel modelini çıkarıp, benzetim ileakciğerdeki basıncın değişimini gözlemlemektir. Elde edilen modelleme ile akciğere ait parametrelerin tahminive benzetimi gerçekleştirilebilir. Böylece akciğerden kaynaklanan rahatsızlıkların teşhisi yapılabilir ve bu,hastalar için etkili bir tedaviye imkan sağlayabilir. Bu bağlamda akciğer mekaniğinin 3 ayrı elektriksel modeliçıkarılmıştır. Bu modeller, tek bölmeli akciğer modeli, çift bölmeli seri ve çift bölmeli paralel modellerdir. Bumodellerin doğruluğunu test etmek için öncelikle MATLAB ortamında mekanik ventilatör modellenmiştir. Dahasonra sabit basınç altında akciğerde oluşan basınç dalga şekilleri incelenmiştir. Benzetimler sonucunda tatminedici sonuçlara ulaşılmıştır.

Obtaining Electrical Model of Lung Mechanics and Pressure Waveforms

In this study, the process of obtaining electrical model of lung which is important organ to maintain life forhuman beings has been implemented. The aim of this study is to obtain electrical model of the lung andinvestigate pressure change in the lung with simulation. With obtained model, estimation and simulation of lungparameters can be implemented. Thus, illnesses caused by the lung diseases can be diagnosed, truly and that canprovide an efficient treatment for patients. In this regards, three different electrical models of the lung have beenrevealed. These models are single compartment model, two series compartment model and two parallelcompartment model. Mechanical ventilator has been firstly modeled in MATLAB to test accuracy of thesemodels. Afterwards, pressure waveforms generated in the lung under fixed pressure have been investigated. As aresult of simulations, it has been arrived for satisfied results.

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  • 1. Perel A, Stock Mc: Handbook of Mechanical Ventilatory support. 1st Ed. Williams and Wilkins,Philadelphia,1992 2. Kirby RR, Banner Mj , Downs JB(Eds), Ventilatory Support 1st Ed Churchill Luingstone inc. New york, 1990 3. Smith RA, Respiratory Care: Mechanical Anesthesia 2nd Ed. Churchill Livingstone,,New York ,1986 4. Pillbeam SP: Mechanical Ventilation: Physiological and Clinical Application. 2nd Ed. , St Louis,Mosby-Year:1992 5. Chatburn R.L., Engineering Principles Applied to Mechanical Ventilator, Proceedings- 25th International Conference-IEEE/EMBS,Vol.1, 406-410, 2003 6. Rios C.A., Tafur J.C., Mathematical Model and Control of the Pneumatic System of a Lung Ventilator Prototype, Proceedings-25th International Conference-IEEE/EMBS, Vol.3, 2776-2779, 2003 7. Zhu H., Guttmann J., Moller K., Control of Respiratory Mechanics with Artificial Neural Networks, Proceedings of the Annual International Conference IEEE-EMBS, 3765- 3768, 2007 8. Kozarski M., Zielinski K., Polko K.J, Bozewicz D., Darowski M., The Hybrid Pneumatic – Numerical Model of Lungs-Metrological Aspects of The Design, XIX IMEKO world Congress Fundamental and Applied Metrology, 2009 9. Diong B., Nazeran H., Nava P., Goldman M., Modelling Human Respiratory Impedance, IEEE Engineering in Medicine and Biology Magazine, 2007 10. Meraz E., Nazeran H., Diong B., Menendez R., Ortiz G., Goldman M., Modelling Human Respiratory Impedance in hispanic Asthmatic Children, Proceedings- 29thAnnunal International Conference-IEEE/EMBS, 2007 11. Saatçi E., Akan A., Respiratory Parameter Estimation in Linear Lung Models, 30th Annual International IEEE EMBS Conference, 20-24 August, 2008 12. Saatçi E., Akan A., Lung Model Parameter Estimation By Unscented Kalman Filter, 29th Annual International IEEE EMBS Conference, 23-26 August ,2007 13. Saatçi E., Akan A., Solunum Parametrelerinin Kestiriminde Genelleştirilmiş Gauss Dağılımlı Hata Modeli, Signal Processing and Communications Application Conference, 9-11 April, 2009 14. Nelson D.S., Strickland J.H., Jannet T.C., Simulation of Fuzzy Control for Management of Respiratory Rate in Assist Control Mechanical Ventilaton Proceedings-19th International Conference-IEEE/EMBS-Vol.3, 1104-1107, 1997 15. Wang C.S., Shaw D., Jih K.S., An Intelligent Control System for Ventilators, Medical Engineering & Physics, Vol. 20, 534-542, 1998 16. Kwok H.F., Linkens D.A., Mahfouf M., Mills G.H., SIVA: Hybrid Knowledge and Model Based Advisory System for Intensive Care Ventilators, IEEE Transactions on Information Technology in Biomedicine, Vol.8 no.2, 161-171, 2004 17. Tzavaras A., Weller P.R., Spyropoulos B., A Neuro-Fuzzy Controller for the Estimation of Tidal Volum and Respiration Frequency Ventilator Settings for COPD Patients Ventilated in Control Mode, Proceedings of the Annual International Conference IEEE-EMBS, 3765- 3768, 2007 18. Güler H., Ata F., Akciğerin modellenmesi ve Bulanık Kontrolü, Elektrik, Elektronik ve Bilgisayar Mühendisliği Konferansı-ELECO, 2-5 Aralık, 2010 19. Güler H., Ata F., Estimation of Inspiration and Expiration Time By Using Fuzzy Control with Respect to Lung’s Dynamics, Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control- ICSCCW, 2009 20. Bates J.H.T, Lung Mechanics-An Inverse Modelling Approach,1st Ed., Cambridge University Press, New York, 2009