Department of Instrumentation, Cochin University of Science and Technology, Kochi, Kerala, India.
Anaesthesia and Critical Care, Aster Medcity, Kochi, Kerala, India.
Comput Methods Programs Biomed. 2019 Jul;176:43-49. doi: 10.1016/j.cmpb.2019.04.014. Epub 2019 Apr 30.
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.
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 FiO which is to be delivered from the ventilator to maintain SaO with in required levels.
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.
Based on the comparison the system is proved to be effective and can be used as assist mode for physicians for effective decision making.
吸入氧分数是呼吸机的任意设定参数之一，对血氧浓度有重要影响。通常，提供呼吸辅助的呼吸机需要手动调整，以提供所需的吸入氧，以保持所需的血氧饱和度水平。维持所需范围内的血氧饱和度至关重要，因为过量的吸入氧会导致高碳酸血症和呼吸性酸中毒，从而增加细胞损伤和死亡的风险。另一方面，血氧饱和度突然下降会导致严重的心脏骤停和癫痫发作。因此，智能实时控制血氧饱和度水平对重症监护病房的患者具有重要意义。
本文通过统计成对分析，从临床数据中找出深度相关的生理参数，以确定模糊变量。使用 R 编程开发了一个基于 Mamdani 模型的咨询模糊控制器，以预测需要从呼吸机输送的 FiO，以维持 SaO 在所需水平内。
使用收集的临床数据的 75%固定模糊模型的模糊变量。剩余的 25%的数据用于检查系统。将系统的预测输出与医生的决策进行比较，发现准确率在 5%以内。
通过比较，该系统被证明是有效的，可以作为医生的辅助模式，以做出有效的决策。
