Research On Processing Method Of Continuous Non-invasive Physiological Indicators

Due to the accelerated pace of life in modern society and the improvement of material living standards,people’s lifestyle and eating habits have changed greatly.Cardiovascular diseases such as hypertension and hyperlipidemia are spreading rapidly in the population,seriously endangering people’s health.They are the main risk factors leading to the onset and death of stroke,coronary heart disease and other diseases in our population.At the same time,due to the decline of fertility rate and the increase of average life expectancy,the degree of aging in China is increasing day by day.For patients with chronic diseases and the elderly,long-term monitoring of their health status is needed.The traditional physiological indicator measurement method has some problems,such as poor comfort and unable to measure continuously,so it is not suitable for people to wear for a long time.According to the demand of wearable physiological indicator continuous monitoring,this paper designs the collection and processing methods of physiological signals such as PPG signal and ECG signal,studies and implements the algorithms of physiological indicators such as blood pressure,blood oxygen saturation,heart rate and respiratory rate,and realizes the continuous non-invasive monitoring of physiological indicators based on the embedded platform.The main work and achievements of this paper are as follows:1.The synchronous acquisition of photoplethysmography(PPG)and single lead electrocardiogram(ECG)signals is designed and realized,and the noise elimination and feature point extraction of PPG and ECG signals are completed,which lays a foundation for the accurate calculation of subsequent physiological indicators.In particular,the PPG feature point extraction algorithm combined with signal quality evaluation,incremental merge segmentation(IMS)algorithm is used to segment the PPG signal and then evaluate the signal quality through kurtosis,skewness and standard deviation,filters out the signals that are greatly disturbed by motion artifacts and ensures the accuracy of feature point extraction.2.The continuous non-invasive monitoring algorithms of blood pressure,blood oxygen saturation,heart rate and respiratory rate are realized by using MATLAB,including blood pressure algorithm based on PPG characteristic parameters and pulse transit time(PTT),blood oxygen saturation algorithm based on empirical formula,heart rate algorithm based on time domain peak detection and frequency domain spectral peak tracking,and respiratory extraction algorithm based on PPG signal and UWB signal.In particular,in order to improve the accuracy and robustness of blood pressure calculation,a blood pressure algorithm based on multiple linear regression and Kalman filter(MLR-KF)is proposed.PPG characteristic parameters with high correlation with blood pressure and PTT are selected to predict blood pressure through multiple linear regression model,and Kalman filter combined with the result of signal quality evaluation algorithm is used to process blood pressure value,which has achieved good results.3.Based on the previously proposed physiological indicator monitoring algorithm,the algorithm with fewer computing resources is selected and optimized by reducing the time complexity and space complexity of the algorithm.Finally,the continuous non-invasive monitoring of physiological indicators such as blood pressure,blood oxygen saturation,heart rate and respiratory rate under the embedded platform with weak computing power is realized.4.The physiological indicators based on PPG and ECG signals of 5 volunteers were calculated by MATLAB,and compared with the reference physiological indicators measured by commercial physiological indicator measuring instruments to verify the accuracy of the physiological indicator algorithms used in this paper.In addition,the MLR-KF blood pressure algorithm was verified by using the mimic public data set.The calculated systolic blood pressure accuracy was 2.02 ± 2.33mmHg and diastolic blood pressure accuracy was 1.39 ±1.62mmHg,which met the blood pressure measurement standard of AAMI.At the same time,the physiological indicator measurement results under the embedded platform are compared with the commercial physiological indicator measurement instruments.The average absolute error of blood pressure is less than 5mmHg and the standard deviation is less than 8mmHg.The average absolute errors of blood oxygen saturation,heart rate and respiratory rate are 0.87%,2.28bpm and 3.00rpm respectively,which verifies the effectiveness of continuous non-invasive monitoring of physiological indicators under the embedded platform.