Research On Key Technologies Of Ventilator Intelligent Ventilation

Mechanical ventilation is one of the most widely used treatments for diseases such as respiratory failure.However,inappropriate ventilator settings can prolong the duration of ventilation treatment,which may increase patient mortality.Therefore,the appropriate selection of ventilator parameters becomes a key factor affecting treatment effectiveness.This paper analyzes the current research status of patient state perception,intelligent decision-making of ventilators,and intelligent control related technologies,summarizes the existing problems in the above research,and proposes an intelligent ventilation method for ventilators from three perspectives:initial parameter and mode settings of ventilators,patient physiological state recognition,and adaptive adjustment of ventilators during ventilation.The specific research content is as follows:(1)Analyzed the application and functional requirements of ventilator intelligent ventilation,and clarified the problems to be solved and the functions that should be possessed by ventilator intelligent ventilation.The overall scheme of intelligent ventilation technology for ventilators has been designed,clarifying how this technology coordinates the relationship between medical staff,patients,and ventilators.We have designed an intelligent ventilation control system for ventilators,which is divided into two parts:parameter setting and parameter adjustment,in order to achieve the initial treatment of patients and adaptive adjustment of ventilator parameters during the treatment process.(2)A mathematical model was developed for analyzing patient blood gas parameters using only non-invasive physiological monitoring parameters.Expert knowledge on the operation and use of ventilators was summarized and organized,and the entire process of ventilator operation was systematically analyzed from start to finish.By using fuzzy systems and neural networks,the ventilation rules in the expert knowledge were translated into control engineering language,and ventilator parameter adjustment curves were generated for different ventilation scenarios.(3)Targeted improvements and optimizations have been made to the traditional fuzzy clustering algorithm.This improved algorithm is used to analyze and process diagnosis and treatment data for respiratory system diseases,helping doctors to differentiate and diagnose different diseases.The diagnostic results are then associated with the clinical indications of mechanical ventilation to achieve parameter and mode settings during the initial stage of ventilation.A fuzzy logic control system has been established and optimized using neural networks.The parameter adjustment curve for the ventilator has been introduced into the optimized control system to achieve real-time,adaptive,stepless adjustment of the ventilator..Finally,to validate the effectiveness of the intelligent ventilation technology of the ventilator,a simulation experimental platform was established,which was divided into two parts:hardware and software.The experiment selected physiological data from several patients who were receiving mechanical ventilation treatment,and used the designed intelligent ventilation system of the ventilator to simulate parameter adjustments,obtaining recommended parameter values.Additionally,clinical doctors were hired to evaluate the same clinical measurement data and provide parameter adjustment suggestions.By comparing the suggested parameter adjustment values between the two,the effectiveness of the intelligent ventilation scheme for ventilators was proven.The implementation of this technology can significantly reduce the training time for medical staff on the operation and use of ventilators,alleviate their work pressure during patient ventilation treatment,and improve the safety and comfort of patients during mechanical ventilation treatment...