A Fuzzy Logic Model-based Advisory System for Mechanical Ventilation_Journal of Biomedical Engineering

Authors：

The Chenggong Affiliated Hospital of Xiamen University, Xiamen 361003, China;

Corresponding author：

Keywords：

mechanical ventilation; pulmonary gas exchange model; fuzzy logic

DOI：

10.7507/1001-5515.20160127

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In the clinical practice, the mechanical ventilation is a very important assisting method to improve the patients' breath. Whether or not the parameters set for the ventilator are correct would affect the pulmonary gas exchange. In this study, we try to build an advisory system based on the gas exchange model for mechanical ventilation using fuzzy logic. The gas exchange mathematic model can simulate the individual patient's pulmonary gas exchange, and can help doctors to learn the patient's exact situation. With the fuzzy logic algorithm, the system can generate ventilator settings respond to individual patient, and provide advice to the doctors. It was evaluated in 10 intensive care patient cases, with mathematic models fitted to the retrospective data and then used to simulate patient response to changes in therapy. Compared to the ventilator set only as part of routine clinical care, the present system could reduce the inspired oxygen fraction, reduce the respiratory work, and improve gas exchange with the model simulated outcome.

Citation： WANGChunfei, CHENZhan. A Fuzzy Logic Model-based Advisory System for Mechanical Ventilation. Journal of Biomedical Engineering, 2016, 33(4): 786-793. doi: 10.7507/1001-5515.20160127 Copy

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3.	MCKINLEY B A, MOORE F A, SAILORS R M, et al. Computerized decision support for mechanical ventilation of trauma induced ARDS:Results of a randomized clinical trial[J]. J Trauma, 2001, 50(3):415-424.
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5.	BOUADMA L, LELLOUCHE F, CABELLO B, et al. Computer-driven management of prolonged mechanical ventilation and weaning:a pilot study[J]. Intensive Care Med, 2005, 31(10):1446-1450.
6.	DOJAT M, HARF A, TOUCHARD D, et al. Clinical evaluation of a computer-controlled pressure support mode[J]. Am J Respir Crit Care Med, 2000, 161(4):1161-1166.
7.	KARBING D S, ALLERØD C, THOMSEN L P, et al. Retrospective evaluation of a decision support system for controlled mechanical ventilation[J]. Med Biol Eng Comput, 2012, 50(1):43-51.
8.	REES S E, ALLERØD C, MURLEY D, et al. Using physiological models and decision theory for selecting appropriate ventilators setting[J]. J Clin Monit Comput, 2006, 20(6):421-429.
9.	REES S E, KJAERGAARD S, PERTHORGAARD P, et al. The automatic lung parameter estimator (ALPE) system:non-invasive estimation of pulmonary gas exchange parameters in 10-15 minutes[J]. J Clin Monit Comput, 2002, 17(1):43-52.
10.	RILEY R L, COURNAND A. 'Ideal' alveolar air and the analysis of ventilation-perfusion relationships in the lungs[J]. J Appl Physiol, 1949, 1(12):825-847.
11.	PETROS A J, DORÉ C J, NUNN J F. Modification of the iso-shunt lines for low inspired oxygen concentrations[J]. Br J Anaesth, 1994, 72(5):515-522.
12.	ANDREASSEN S, REES S E. Mathematical models of oxygen and carbon dioxide storage and transport:interstitial fluid and tissue stores and whole-body transport[J]. Crit Rev Biomed Eng, 2005, 33(3):265-298.
13.	REES S E, ANDREASSEN S. Mathematical models of oxygen and carbon dioxide storage and transport:the acid-base chemistry of blood[J]. Crit Rev Biomed Eng, 2005, 33(3):209-264.
14.	KARBING D S, KJÆRGAARD S, ANDREASSEN S, et al. Minimal model quantification of pulmonary gas exchange in intensive care patients[J]. Med Eng Phys, 2011, 33(2):240-248.
15.	WEST J B. Respiratory physiology-the essentials[M]. 6th ed. USA:Lippincott Williams & Wilkins, 2000.
16.	WEIBEL E R. Morphometry of the human lung[M]. Berling:Springer-Verlag, 1963.
17.	DE GRAY L, RUSH E M, JONES J G. A noninvasive method for evaluating the effect of thoracotomy on shunt and ventilation perfusion inequality[J]. Anaesthesia, 1997, 52(7):630-635.
18.	MEADE M O, COOK D J, GUYATT G H, et al. Ventilation strategy using low tidal volumes, recruitment maneuvers, and high positive end-expiratory pressure for acute lung injury and acute respiratory distress syndrome:a randomized controlled trial[J]. JAMA, 2008, 299(6):637-645.
19.	TALMOR D, SARGE T, MALHOTRA A, et al. Mechanical ventilation guided by esophageal pressure in acute lung injury[J]. N Engl J Med, 2008, 359(20):2095-2104.
20.	MEHTA S, COOK D J, SKROBIK Y, et al. A ventilator strategy combining low tidal volume ventilation, recruitment maneuvers, and high positive end-expiratory pressure does not increase sedative, opioid, or neuromuscular blocker use in adults with acute respiratory distress syndrome and May improve patient comfort[J]. Ann Intensive Care, 2014, 4:33.
21.	RUBENFELD G D, COOPER C, CARTER G, et al. Barriers to providing lung-protective ventilation to patients with acute lung injury[J]. Crit Care Med, 2004, 32(6):1289-1293.

1. IOTTI G V. Closed-loop control mechanical ventilation[J]. Respir Care Clin N Am, 2001(7):341-515.
2. BERNSTEIN D B, NGUYEN B, ALLEN G B, et al. Elucidating the fuzziness in physician decision making in ARDS[J]. J Clin Monit Comput, 2013, 27(3):357-363.
3. MCKINLEY B A, MOORE F A, SAILORS R M, et al. Computerized decision support for mechanical ventilation of trauma induced ARDS:Results of a randomized clinical trial[J]. J Trauma, 2001, 50(3):415-424.
4. KWOK H F, LINKENS D A, MAHFOUF M, et al. Rule-base derivation for intensive care ventilator control using ANFIS[J]. Artif Intell Med, 2003, 29(3):185-201.
5. BOUADMA L, LELLOUCHE F, CABELLO B, et al. Computer-driven management of prolonged mechanical ventilation and weaning:a pilot study[J]. Intensive Care Med, 2005, 31(10):1446-1450.
6. DOJAT M, HARF A, TOUCHARD D, et al. Clinical evaluation of a computer-controlled pressure support mode[J]. Am J Respir Crit Care Med, 2000, 161(4):1161-1166.
7. KARBING D S, ALLERØD C, THOMSEN L P, et al. Retrospective evaluation of a decision support system for controlled mechanical ventilation[J]. Med Biol Eng Comput, 2012, 50(1):43-51.
8. REES S E, ALLERØD C, MURLEY D, et al. Using physiological models and decision theory for selecting appropriate ventilators setting[J]. J Clin Monit Comput, 2006, 20(6):421-429.
9. REES S E, KJAERGAARD S, PERTHORGAARD P, et al. The automatic lung parameter estimator (ALPE) system:non-invasive estimation of pulmonary gas exchange parameters in 10-15 minutes[J]. J Clin Monit Comput, 2002, 17(1):43-52.
10. RILEY R L, COURNAND A. 'Ideal' alveolar air and the analysis of ventilation-perfusion relationships in the lungs[J]. J Appl Physiol, 1949, 1(12):825-847.
11. PETROS A J, DORÉ C J, NUNN J F. Modification of the iso-shunt lines for low inspired oxygen concentrations[J]. Br J Anaesth, 1994, 72(5):515-522.
12. ANDREASSEN S, REES S E. Mathematical models of oxygen and carbon dioxide storage and transport:interstitial fluid and tissue stores and whole-body transport[J]. Crit Rev Biomed Eng, 2005, 33(3):265-298.
13. REES S E, ANDREASSEN S. Mathematical models of oxygen and carbon dioxide storage and transport:the acid-base chemistry of blood[J]. Crit Rev Biomed Eng, 2005, 33(3):209-264.
14. KARBING D S, KJÆRGAARD S, ANDREASSEN S, et al. Minimal model quantification of pulmonary gas exchange in intensive care patients[J]. Med Eng Phys, 2011, 33(2):240-248.
15. WEST J B. Respiratory physiology-the essentials[M]. 6th ed. USA:Lippincott Williams & Wilkins, 2000.
16. WEIBEL E R. Morphometry of the human lung[M]. Berling:Springer-Verlag, 1963.
17. DE GRAY L, RUSH E M, JONES J G. A noninvasive method for evaluating the effect of thoracotomy on shunt and ventilation perfusion inequality[J]. Anaesthesia, 1997, 52(7):630-635.
18. MEADE M O, COOK D J, GUYATT G H, et al. Ventilation strategy using low tidal volumes, recruitment maneuvers, and high positive end-expiratory pressure for acute lung injury and acute respiratory distress syndrome:a randomized controlled trial[J]. JAMA, 2008, 299(6):637-645.
19. TALMOR D, SARGE T, MALHOTRA A, et al. Mechanical ventilation guided by esophageal pressure in acute lung injury[J]. N Engl J Med, 2008, 359(20):2095-2104.
20. MEHTA S, COOK D J, SKROBIK Y, et al. A ventilator strategy combining low tidal volume ventilation, recruitment maneuvers, and high positive end-expiratory pressure does not increase sedative, opioid, or neuromuscular blocker use in adults with acute respiratory distress syndrome and May improve patient comfort[J]. Ann Intensive Care, 2014, 4:33.
21. RUBENFELD G D, COOPER C, CARTER G, et al. Barriers to providing lung-protective ventilation to patients with acute lung injury[J]. Crit Care Med, 2004, 32(6):1289-1293.

Journal of Biomedical Engineering

A Fuzzy Logic Model-based Advisory System for Mechanical Ventilation

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