Research Of Non-invasive Arterial Risk Assessment Method Basing On Peripheral Pulse Wave Velocity

Early detection and timely control is the key measure to treating. And the early manifestation of this disease is arterial elasticity reducing, and artery gradual hardening. Pulse wave signal analysis is one of the methods of quantitative assessment of atherosclerosis.Therefore, in this paper, we hoped extracting useful information from pulse wave by platform we made to evaluate potential risks in human artery and lay the foundation for the subsequent application.In the paper, we comprehensive analyzed the current status of developments and improvements of domestic and foreign non-invasive arterial risk assessment, and proposed our issue- research of non-invasive arterial risk assessment method basing on peripheral pulse wave velocity. Specifically, we calculated the finger- toe pulse wave velocity, pulse pressure(PP) and ankle- brachial index(ABI) to predict. The main contents of paper included the following aspects: firstly, we described the current serious situation of domestic cardiovascular disease, and prevention and treatment is imminent. The precursor of cardiovascular disease is arterial elastic function decline occurs gradually hardening or obstruction of the structural function damage. Secondly, we introduced and analyzed the traditional measurement method(combined with pulse waves from different parts of body to assess the degree of elasticity) and the well-known devices, both domestic and foreign, and put forward a new measurement solution that defining a new index named finger- toe pulse wave velocity(ft PWV) to replace the existing sectional pulse wave velocity which can be continuous, uninterrupted recording pulse wave and simple in operation. Thirdly, we described the whole building process of system from hardware to software. The hardware part included detailed design and specification of power supply, signal acquisition module and WIFI module. According to multi-signal processing mechanism, we designed a main control board, used STM32F427VGT6 as MCU, including its main control program on chip anddesktop software on PC. The main control program realized command, control and coordination of the various modules simultaneously, and received instruction from PC and data combination from all modules. PC desktop software realized user command input and displaying single lead ECG, pulse wave and blood pressure measurements. Acquired data was imported to MATLAB for processing that writing Savizky- Golay filter and moving average filter to remove noise, and smooth prior to trend algorithm to remove baseline drift.Eventually, we obtained high quality pulse wave signal and extract time-domain feature points from it to calculate pulse transit time difference between finger and toes, and combined with finger-toe length difference to deduce finger-toe pulse wave velocity. At last, we verified our system by experiments. First, we collected 100 cases of repeated measurement results from 10 volunteers. The C.V value of results is 0.8% that our system had great reliability and repeatability. Second, we compared 600 test results from potential risk group(30 elderly people) and healthy control group(30 young people). The t value of left ft PWV is 9.73 and the right is 9.91 that had a significant difference that had an application value in cardiovascular risk prediction. Third, we measured the arms and ankles' blood pressure of the two groups above, and calculated Pulse. The relative difference of PP is 1.26%, and the ABI is0.85% that PP and ABI could be as accessory factors of our platform to screening the occlusion part for severe atherosclerosis. The results above showed that the platform and algorithm we designed achieved the desired goals. In future, we will continue to improve our measurement system and the application of our calculating parameters, such as system miniaturization, direct calculation of parameters and big data analytics, to lay the foundation for further application.