Development Of Multiple Physiological Parameters Monitoring System

Human physiological signals can reflect the health status of the human body,and real-time monitoring of physiological physiological signal parameters can be used as the main basis for disease analysis and diagnosis.The human body physiological parameter monitoring system monitors human blood pressure,electrocardiogram,blood oxygen and body temperature,which is of great significance for early detection,early control and early diagnosis of clinical cardiac diseases.The system uses STM32 single-chip microcomputer as the control core,collects physiological signals such as ECG,blood pressure and pulse wave,realizes data storage and uploads it to the upper computer,and the upper computer analyzes and processes the data.The physiological signal is a weak electrical signal,which is susceptible to external interference and noise during the acquisition process,causing errors in the measurement process,and the central electrical signal interference is particularly serious.The system uses the morphological filtering method in the upper computer to process the ECG signal to achieve signal denoising.The detection of myocardial infarction diseases is realized by a neural network-based method.Combined with the detection of blood pressure and blood oxygen saturation,it has positive significance for the diagnosis and research of clinical heart health.The main points of this paper are as follows:1.Study and analyze the mechanism and characteristics of physiological signals such as blood pressure,ECG,blood oxygen and body temperature,and design a multi-physiological parameter monitoring system for human body.The continuous measurement of blood oxygen saturation based on the reflective photoelectric sensor is realized;based on the potential difference theory,the detection of the ECG signal is realized by a single-stage pressurized lead method.2.A cuffless blood pressure measuring device based on pulse wave transit time was developed to realize the detection of blood pressure without cuff.3.Set the strict threshold and loose threshold to optimize the differential threshold method,and realize the characteristic band extraction of ECG signals through the optimized differential threshold method.Use the morphological algorithmto filter the baseline drift of ECG signals and implement BP neural network algorithm.Detection of myocardial infarction.4.The upper computer system is designed to display the collected physiological signals in real time and analyze and process them.5.Test the entire system.