Development Of Urodynamic Monitoring System

Lower urinary tract dysfunction is caused by bladder or urethral dysfunction and is usually defined as storage,voiding,and post-micturition symptoms.The urodynamic analyzer scientifically evaluates and diagnoses lower urinary tract dysfunction by collecting urodynamic signals,and can also be used for acute kidney injury and intraabdominal hypertension monitoring in critically ill patients.With the increasing prevalence of lower urinary tract dysfunction,the demand for urodynamic analyzers is increasing in the medical industry.At present,domestic and foreign urodynamic analyzers are expensive,with low measurement accuracy,low integration,and limitations such as trauma,cross infection,and complications.Based on this,this study realized a low-cost,portable,accurate and reliable urodynamic monitoring system,completed the hardware and software design of the system,developed and verified a lower urinary tract dysfunction diagnosis algorithm based on machine learning,and verified the reliability of the monitoring system through simulation experiments.This article focuses on the research background and current research status of urodynamic monitoring system at home and abroad.It provides a detailed introduction to the composition,pressure acquisition,and urinary flow rate acquisition principles of the urodynamic monitoring system.It also analyzes the requirements for key parameters of urodynamic and lower urinary tract dysfunction,and proposes the overall design and implementation plan of the urodynamic monitoring system.In terms of system hardware design,the key device selection and hardware circuit design have been completed,including the main processor,power supply module,urine flow rate acquisition circuit,pressure acquisition circuit and motor drive circuit.In terms of system software design,the lower computer software design of the acquisition circuit and the embedded software design based on the real-time operating system μC/OS have been completed,achieving application layer software functions such as humancomputer interaction interface,patient management module,alarm function module,data query and storage,and mode control.In terms of algorithm implementation,an overview of the diagnostic algorithm for lower urinary tract dysfunction based on machine learning was proposed.The characteristics of pressure and urinary flow rate signals were analyzed in detail,and filters were designed to reduce the impact of noise or motion artifacts on signal quality.Urodynamic signal parameter calculation and feature extraction were completed,and a diagnostic classification model for lower urinary tract dysfunction based on machine learning was constructed.The experimental results showed that the use of support vector machines has the best diagnostic performance for lower urinary tract dysfunction,with an average accuracy rate of 90.9%.By integrating and testing the system hardware and software,the prototype development of the urodynamic monitoring system has been completed,and the hardware testing indicators have met the standard requirements.A simulated experimental environment has been established to verify the reliability and stability of the urodynamic monitoring system.The experimental results show that the hardware and software functions of the system run stably,and the measurement errors of urodynamic parameters meet the expected design goals,providing support for subsequent engineering design and clinical application.