A manual measurement of blood vessels diameter is a conventional component of routine visual assessment of microcirculation, say, during optical capillaroscopy. However, many modern optical methods for blood flow measurements demand the reliable procedure for a fully automated detection of vessels and estimation of their diameter that is a challenging task. Specifically, if one measure the velocity of red blood cells by means of laser speckle imaging, then visual measurements become impossible, while the velocity-based estimation has their own limitations. One of promising approaches is based on fast switching of illumination type, but it drastically reduces the observation time, and hence, the achievable quality of images. In the present work we address this problem proposing an alternative method for the processing of noisy images of vascular structure, which extracts the mask denoting locations of vessels, based on the application of the continuous wavelet transform with the Morlet wavelet having small central frequencies. Such a method combines a reasonable accuracy with the possibility of fast direct implementation to images. Discussing the latter, we describe in details a new MATLAB program code realization for the CWT with the Morlet wavelet, which does not use loops completely replaced with element-by-element operations that drastically reduces the computation time.
Photoplethysmography is an optical technique that can be used to detect blood volume changes and to measure important physiological parameters. This is low cost and non-invasive technique. However, one has to apply sensor directly to the skin. In this regard, the development on remote mothods receives the growing attention, such as imaging photoplethysmography (iPPG). Note, most of public-available iPPG systems are based on smartphone-embedded cameras, and thus have a sample frequency about 30-60 frames per second, which is enough for heart rate measurements, but may be too low for some more advanced usages of this technique. In our work, we describe the attempt to use smartphone-based iPPG technique aimed to measure the tiny mismatch in RR interval data series recorded from left and right arms. We use the transmission mode iPPG, in which the light transmitted through the medium of finger is detected by a web-camera opposite the LED source. The computational scheme by processing and analysis of the received signal was implemented using MATLAB language (MathWork Inc. in the United States). We believe that further development of our approach may lead to fast and low cost method to access the state of the sympathetic nervous system.
Assessment of pulse waves that recorded in the microvascular bed when the heart throwing blood appears to be the essential diagnostic method. The conventional non-invasive methods are mostly based on measurement of pulse wave velocity (PWV) which was proved to be the predictor of cardiovascular system state. Photoplethysmography (PPG) is a simple and low-cost optical technique that can be used to detect blood volume changes in the microvascular bed of tissue. Since many factors contribute to PWV formation, it shows considerable variability and sensitive to the current physiological state. Traditional mathematical methods that examine this variability in the frequency domain, such as Fourier analysis, not always the best choice since the non-stationary features of PWV signal. A relatively new, but already popular tool, Wavelet transform, allows multiresolution analysis in time-frequency domain of non-stationary signals. In our work we apply Wavelet Cross Spectrum (WCS) and Wavelet-Based Coherence (WBC) to reveal the similarities between two PWV time series recorded simultaneously from left and right arms. We find that the degree correlation and the time lag between these signals considerably depend on frequency range. On this basis, we hypothesize the systemic (neurogenic) origin of high-frequency (0.2 Hz) PWV variations.
The elimination of low-frequency noise of breath and motion artifacts is one of the most difficult challenges of preprocessing rheographic signal. The data filtering is the conventional way to separate useful signal from noise and interferences. Conventionally, linear filtering is used to easy design and implementation. However, in some cases such techniques are difficult, if possible, to apply, since the data frequency range is overlapped with one of interferences. Specifically, it happens in aortic rheography, where some breathing process and pulmonary blood flow contributions are unavoidable. We suggest an alternative approach for breathing interference reduction, based on adaptive reconstruction of baseline deviation. Specifically, the computational scheme based on multiple calculation of Akima splines is suggested, implemented using C# language and validated using surrogate data. The applications of proposed technique to the real data processing deliver the better quality of aortic valve opening detection.