Noninvasive continuous blood pressure estimation is a promising alternative to minimally invasive blood pressure measurement using cuff and invasive catheter measurement, because it opens the way to both long-term and continuous blood pressure monitoring in ecological situation. The most current estimation algorithm is based on pulse transit time measurement where at least two measured signals need to be acquired. From the pulse transit time values, it is possible to estimate the continuous blood pressure for each cardiac cycle. This measurement highly depends on arterial properties which are not easily accessible with common measurement techniques; but these properties are needed as input for the estimation algorithm. With every change of input arterial properties, the error in the blood pressure estimation rises, thus a periodic calibration procedure is needed for error minimization. Recent research is focused on simplified constant arterial properties which are not constant over time and uses only linear model based on initial measurement. The elaboration of continuous calibration procedures, independent of recalibration measurement, is the key to improving the accuracy and robustness of noninvasive continuous blood pressure estimation. However, most models in literature are based on linear approximation and we discuss here the need for more complete calibration models.

Wavelet transform (WT) is a commonly used method for noise suppression and feature extraction from biomedical images. The selection of WT system settings significantly affects the efficiency of denoising procedure. This comparative study analyzed the efficacy of the proposed WT system on real 292 ultrasound images from several areas of interest. The study investigates the performance of the system for different scaling functions of two basic wavelet bases, Daubechies and Symlets, and their efficiency on images artificially corrupted by three kinds of noise. To evaluate our extensive analysis, we used objective metrics, namely structural similarity index (SSIM), correlation coefficient, mean squared error (MSE), peak signal-to-noise ratio (PSNR) and universal image quality index (Q-index). Moreover, this study includes clinical insights on selected filtration outcomes provided by clinical experts. The results show that the efficiency of the filtration strongly depends on the specific wavelet system setting, type of ultrasound data, and the noise present. The findings presented may provide a useful guideline for researchers, software developers, and clinical professionals to obtain high quality images.

This article investigates a prospective capacitive sensing method for the measurement of physical characteristics of a pulsatile liquid flow. It was tested on a physical vascular model. The volume pulse wave sensor holds a high sensitivity and thus great potential in the field of industrial applications and also in biomedical applications where we also encounter a pulsating flow of blood. Two types of capacity sensors were developed and tested on our blood vessel model, currently used for the simulation of pulse wave propagation with the possibility of changes in vessel properties and many setups are reached for testing different pulse wave propagations. The newly developed capacitive sensor detects the capacity change of pulsating liquid flow in the vascular model. Data from the capacitive sensor are compared with two reference sensors, one for direct pressure sensing and the second for noncontact optical sensing of changes in diameter. The signal processing is used for all three types of sensors and analysis in the time and frequency domain to determine the accuracy. The use of capacitive sensors is promising since it can measure a negligible change in the diameter of the vessel in the range of tens of femtofarads (fF). We can confirm that the high precision measurement is dependent on the environment, which can cause the capacity change, but with proper shielding it can be eliminated.