An intelligent control system for ventilators

Wang, Chien Sheng; Shaw, Dein; Jih, Kuen Shan

doi:10.1016/s1350-4533(98)00048-4

Cited by 11 publications

(6 citation statements)

References 13 publications

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“…where x ¯ is the arithmetic mean, S 2 is the variance, t is the test formula, x is the investigated group and n is the number of data points in group. Lung dynamics were calculated from equations (4)–(6) 30,31…”

Section: Resultsmentioning

confidence: 99%

The comparison of manual and LabVIEW-based fuzzy control on mechanical ventilation

Güler

Ata

2014

Proc Inst Mech Eng H

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The aim of this article is to develop a knowledge-based therapy for management of rats with respiratory distress. A mechanical ventilator was designed to achieve this aim. The designed ventilator is called an intelligent mechanical ventilator since fuzzy logic was used to control the pneumatic equipment according to the rat's status. LabVIEW software was used to control all equipments in the ventilator prototype and to monitor respiratory variables in the experiment. The designed ventilator can be controlled both manually and by fuzzy logic. Eight female Wistar-Albino rats were used to test the designed ventilator and to show the effectiveness of fuzzy control over manual control on pressure control ventilation mode. The anesthetized rats were first ventilated for 20 min manually. After that time, they were ventilated for 20 min by fuzzy logic. Student's t-test for p < 0.05 was applied to the measured minimum, maximum and mean peak inspiration pressures to analyze the obtained results. The results show that there is no statistical difference in the rat's lung parameters before and after the experiments. It can be said that the designed ventilator and developed knowledge-based therapy support artificial respiration of living things successfully.

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Section: Resultsmentioning

confidence: 99%

The comparison of manual and LabVIEW-based fuzzy control on mechanical ventilation

Güler

Ata

2014

Proc Inst Mech Eng H

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“…Artificial ventilation plays a very important role in the treatment of patients in Intensive Care Unit (ICU). In the literature, there have been several computer-based models [26,27] proposed to automatically control the ventilation, based on a patient's current status. Our task is to predicate the one hour later FiO2 value, based on the current values of FiO2, RR, SaO2 and PEEP.…”

Section: Icu Ventilator Control Datasetmentioning

confidence: 99%

A Modified Generalized RBF Model with EM-based Learning Algorithm for Medical Applications

Wahab

Quek

2006

19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06)

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Radial Basis Function (RBF) has been widely used in different fields, due to its fast learning and interpretability of its solution. One problem of classical RBF is that it suffers from curse of dimensionality that the number of basis functions will explode with the increase of dimensions in the dataset. This explosion usually impairs the usefulness and interpretability of RBF, especially in medical applications, where the dimensions of dataset are high and the explanations of solutions are important. In this paper, we propose a generalized RBF (GRBF) model to reduce the number of basis functions and thus alleviate curse of dimensionality. An EM-based training algorithm is also introduced, which uses fewer parameters compared to some classical supervised learning methods. This will make the learning process simpler and more convenient in practice. Moreover, GRBF trained by the new algorithm has an apparent statistical meaning. Experimental results show potentials for real-life applications.

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“…The patients are usually connected with the ventilator through an endotracheal tube (ETT). Air is moved to the patients' lung by ventilators according to settings predetermined by clinicians [1]. The air is made up of several gases and contains 21% oxygen.…”

Section: Introductionmentioning

confidence: 99%

Estimation of inspiration and expiration time by using fuzzy control with respect to lung's dynamics

Güler

Ata

2009

2009 Fifth International Conference on Soft Computing, Computing With Words and Perceptions in System Analysis, Decision and Co

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In this study, estimation of inspiration and expiration time in positive pressure ventilator, which is used in intensive care units and implements patient's artificial respiratory system that has been disturbed is performed with fuzzy logic controller. The goal of this system is to reduce workload of anaesthetist. Since he/she has many patients in intensive care units so he has no so much time each patients. Fuzzy logic controller determines time of inspiration and expiration. By estimating inspiration and expiration time, lung's dynamics which are resistance and compliance are considered. Performed system is run in the pressure control ventilation (PCV) which is one of the most used modes among other ventilation modes.

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An intelligent control system for ventilators

Cited by 11 publications

References 13 publications

The comparison of manual and LabVIEW-based fuzzy control on mechanical ventilation

The comparison of manual and LabVIEW-based fuzzy control on mechanical ventilation

A Modified Generalized RBF Model with EM-based Learning Algorithm for Medical Applications

Estimation of inspiration and expiration time by using fuzzy control with respect to lung's dynamics

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