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Two methods for measuring pulse wave using either a pressure or an optical sensor are studied and compared. A model for determining the optimal external force for restraining the pressure sensor is proposed. Pulse waves are measured on 20 volunteers. The restraint force on the pressure sensor is slowly varied and recorded using a piezoelectric film sensor for each person until it reaches a so-called optimal acquisition pressure under which the pulse wave has the greatest amplitude. The optimal pressure is then analyzed using polynomial regression with the body-mass index(BMI). In addition, the pulse waves are decomposed in both the time and frequency domains to identify and extract the features corresponding to physiological activities. Pulse waves measured with the optical sensor method suffer from feature point loss where certain points are submerged and indistinguishable. The pressure sensor method can reveal the feature points clearly, but the waveform exhibits variations if the external force applied isn′t a constant. With the application of optimal acquisition force set by our model, the latter method is able to detect pulse wave with a high signal-to-noise ratio. The dual domain analysis is shown to extract features that can be later used for continuous blood pressure monitoring.

Blood pressure (BP) is one of the indispensable elements of physiological health characteristics and a significant indicator for predicting and diagnosing hypertension and cardiovascular diseases. This paper proposes a two-domain fusion model to estimate BP continuously from pulse wave acquired with a pressure sensor. Method. The optimal external pressure applied on the pressure sensor is first determined in order to capture pulse wave in the radial artery. The captured pulse wave is then processed in both the time and frequency domains via filtering and fast Fourier transform. Finally, a set of features are extracted from these two domains and input into a neural network along with blood pressure values measured by a commercial sphygmomanometer for training. The model is then tested on new data for accuracy evaluation. Results. The proposed two-domain fusion method achieved a high degree of accuracy in measuring blood pressure. [ABSTRACT FROM AUTHOR]