Analysis of parameters affecting blood oxygen saturation and modeling of fuzzy logic system for inspired oxygen prediction. (July 2019)

• Record Type: Journal Article
• Authors: Radhakrishnan, Sita; Nair, Suresh G; Isaac, Johney
• Is Part Of: Computer methods and programs in biomedicine. Volume 176(2019)
• Page Start: 43
• Page End: 49
• Publication Date: 2019-07
• Subjects: Oxygen saturation -- Mechanical ventilation -- Data collection -- Fuzzy logic; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.04.014
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: The importance of automation of mechanical ventilator by designing a fuzzy logic predictive system. The use of real time values of pulse oximetry along with periodical readings of arterial blood gas analysis for decision making. The attention to the continuous monitoring and control of patients in intensive care units by the development of fuzzy logic predictive system. Abstract: Background and Objective: Fraction of Inspired Oxygen is one of the arbitrary set ventilator parameters which has critical influence on the concentration of blood oxygen. Normally mechanical ventilators providing respiratory assistance are tuned manually to supply required inspired oxygen to keep the oxygen saturation at the desired level. Maintaining oxygen saturation in the desired limit is so vital since excess supply of inspired oxygen leads to hypercapnia and respiratory acidosis which lead to increased risk in cell damage and death. On the other side a sudden drop in oxygen saturation will lead to severe cardiac arrest and seizure. Hence intelligent real time control of blood oxygen level saturation is highly significant for patients in intensive care units. Methods: This paper gives statistical pair wise analysis for finding out deeply correlated physiological parameters from clinical data for fixing fuzzy variables. An advisory fuzzy controller using Mamdani model is developed with R programming to predict FiO2 which is to be delivered from the ventilator to maintain SaO2 with in required levels. Results: Fuzzy variables for the fuzzy model is fixed using 75% of the clinical data collected. Remaining 25% of the data is used for checking the system. Compared the predictive output of the system with physicians' decisions and found to be accurate with less than five percentage error. Conclusions: Based on the comparison the system is proved to be effective and can be used as assist mode for physicians for effective decision making.