Research Of Continuous Non-Invasive Blood Pressure Measurement Method

Blood pressure which can reflect the heart and the circulatory system functional status is one of the important human physiological parameters. The blood pressure is not remain constant, it will be affected by many factors, such as emotional, sports, a variety of external and internal stimulations, so a convenient and effective continuous non-invasive blood pressure measurement have a great significance in the clinical detection of blood pressure. Relative to the current clinical use of the direct measurement and the Korotkoff method, continuous non-invasive blood pressure measurement get rid of the cuff shackles of the traditional blood pressure measurements and is able to monitor blood pressure non-invasively in a long time.In this paper, the continuous non-invasive blood pressure measurement method mentioned is based on the theory that PWTT have a linear relationship with human arterial blood pressure, and thus to prove feasibility of calculating body blood pressure through PWTT. Firstly, we studied three pulse wave transit time extraction method, for example, we can use ECG and PPG, dual-PPG, accelerated PPG to extract the pulse wave transit time. When collecting physiological signals, since the interference generated by electronic components, electromagnetic interference, ambient light changes, body’s breathing and movement and other factors, the waveform often contains a lot of high-frequency interference and baseline drift. In this case, we use the median filtering algorithm and wavelet transform method to remove high-frequency interference and baseline drift. When collecting physiological signals, since the interference generated by electronic components, electromagnetic interference, ambient light changes, body’s breathing and movement and other factors, the waveform often contains a lot of high-frequency interference and baseline drift. In this case, we use the median filtering algorithm and wavelet transform method to remove high-frequency interference and baseline drift. Then according to the three kinds of PWTT extraction methods, an improved feature point locating algorithm was designed which is efficient and accurate. At last, sample data collected have been correlation analyzed, regression analyzed. According to the three PWTT extraction algorithms, PWTT extracted pulse wave Time and systolic blood pressure have been linear fitted, and also the results have been error analyzed and cross-validated.