Based On The Study Of Factors Affecting PTT Non-invasive Blood Pressure Measurement

Blood pressure(BP)is one of the most important physiological parameters,which is important for diagnosing disease,observation on the treatment,prognosis in clinic medicine,and it reflects the functional status of the human heart and blood vessel.BP will change with various stimulus such as physiological cycle,personal emotion or other factors,which reveals a distinct fluctuation.Because the factors such as physical condition and biorhythms have influence on BP,the measuring method of non-invasive blood pressure based on pulse wave translation time(PTT)is relatively stable over a period of time for single individual.But there are many difference for different individuals.So the measuring method of non-invasive blood pressure based on PTT is not widely used.In view of the current situation,the paper comes up with the prediction model of non-invasive blood pressure based on PTT and characteristic parameters of pulse wave.The research introduces the characteristic parameters of pulse wave on the basis of using PTT to compute BP,and yet the parameters also directly and indirectly reflects the difference between different individuals to some extent.According to correlation analysis,we have elected the characteristic parameter of high relative coefficient,and established the prediction model of systolic and diastolic blood pressure by using least square method to train.Compared with the ohm of dragon electron sphygmomanometer,The standard deviation of systolic blood pressure is 5.16mmHg and the standard deviation of diastolic blood pressure is 5.27mmHg.The prediction results satisfies the design standards of electronic sphygmomanometer of AMMI.And then we make some verified experiments,the standard deviation of systolic blood pressure is 2.11mmHg,and the standard deviation of diastolic blood pressure is 2.00mmHg.the results indicate that introducing the optimized characteristic parameters of pulse wave and PTT into the prediction model of continuous BP contributes to improving the accuracy of prediction model of BP and clinical application of noninvasive continuous blood pressure monitoring.