The real-time artery diameter waveform assessment during cardio cycle can allow the measurement of beat-to-beat pressure change and the long-term blood pressure monitoring. The aim of this study is to develop a self-calibrated bio-impedance-based sensor, which can provide regular measurement of the blood-pressure-dependence time variable parameters such as the artery diameter waveform and the elasticity. This paper proposes an algorithm based on analytical models which need prior geometrical and physiological patient parameters for more appropriate electrode system selection and hence location to provide accurate blood pressure measurement. As a result of this study, the red cell orientation effect contribution was estimated and removed from the bio-impedance signal obtained from the artery to keep monitoring the diameter waveform correspondence to the change of blood pressure.

A venipuncture is the most common non-invasive medical procedure, and is frequently used with patients; however, a high probability of post-injection complications accompanies intravenous injection. The most common complication is a hematoma, which is associated with puncture of the uppermost and lowermost walls. To simplify and reduce complications of the venipuncture procedure, and as well as automation of this process, a device that can provide information of the needle tip position into patient’s tissues needs to be developed. This paper presents a peripheral vascular puncture control system based on electrical impedance measurements. A special electrode system was designed to achieve the maximum sensitivity for puncture identification using a traditional needle, which is usually used in clinical practice. An experimental study on subjects showed that the electrical impedance signal changed significantly once the standard needle entered the blood vessel. On basis of theoretical and experimental studies, a decision rule of puncture identification based on the analysis of amplitude-time parameters of experimental signals was proposed. The proposed method was tested on 15 test and 9 control samples, with the results showing that 97% accuracy was obtained.

The electrical impedance myography method is widely used in solving bionic control problems and consists of assessing the change in the electrical impedance magnitude during muscle contraction in real time. However, the choice of electrode systems sizes is not always properly considered when using the electrical impedance myography method in the existing approaches, which is important in terms of electrical impedance signal expressiveness and reproducibility. The article is devoted to the determination of acceptable sizes for the electrode systems for electrical impedance myography using the Pareto optimality assessment method and the electrical impedance signals formation model of the forearm area, taking into account the change in the electrophysical and geometric parameters of the skin and fat layer and muscle groups when performing actions with a hand. Numerical finite element simulation using anthropometric models of the forearm obtained by volunteers’ MRI 3D reconstructions was performed to determine a sufficient degree of the forearm anatomical features detailing in terms of the measured electrical impedance. For the mathematical description of electrical impedance relationships, a forearm two-layer model, represented by the skin-fat layer and muscles, was reasonably chosen, which adequately describes the change in electrical impedance when performing hand actions. Using this model, for the first time, an approach that can be used to determine the acceptable sizes of electrode systems for different parts of the body individually was proposed.

Knowledge of renal blood circulation is considered as an important physiological value, particularly for fast detection of acute allograft rejection as well as the management of critically ill patients with acute renal failure. The electrical impedance signal obtained from kidney with an appropriate electrode system and optimal electrode system position regarding to the kidney projection on skin surface reflects the nature of renal blood circulation and tone of renal blood vessels. This paper proposes a specific numerical modelling based on prior information from MRI-data. The numerical modelling was conducted for electrical impedance change estimation due to renal blood distribution. The proposed model takes into the account the geometrical and electrophysiological parameters of tissues around the kidney as well as the actual blood distribution within the kidney. The numerical modelling had shown that it is possible to register the electrical impedance signal caused by renal blood circulation with an electrode system commensurate with the size of kidney, which makes it possible to reduce the influence of surrounding tissues and organs. Experimental studies were obtained to prove the numerical modelling and the effectiveness of developed electrode systems based on the obtained simulation results. The obtained electrical impedance signal with the appropriate electrode system shows very good agreement with the renal blood change estimated using Doppler ultrasound. For the measured electrical impedance signal, it is possible to obtain the amplitude-time parameters, which reflect the hemodynamic characteristics of the kidneys and used in diagnostics, which is the subject of further research.

Statement of the problem. Diagnosis and monitoring of mucopolysaccharidoses (MPS), a group of hereditary lysosomal storage diseases, remain challenging due to the variability of clinical manifestations and the need for an integrated approach. Advances in molecular genetic and neuroimaging methods require systematization and analysis of modern approaches, including the integration of advanced technologies such as artificial intelligence (AI) and machine learning (ML), to improve the accuracy of diagnosis, especially when assessing brain changes. There is a need to standardize methods and develop new algorithms for quantitative data analysis and assessment of disease dynamics. Objective. To conduct a comprehensive analysis of modern methods for diagnosing and monitoring MPS, covering laboratory, instrumental and neuroimaging approaches; to consider the use of advanced technologies (ML, AI) to improve the accuracy of detection of pathological changes in the brain; to pay attention to the role of quantitative image analysis, segmentation methods and their application in automated systems; to describe the main biomarkers and problems of data interpretation; to assess the need for an interdisciplinary approach and standardization of methods for the formation of promising areas for the development of diagnostics and monitoring of MPS. Results. A comprehensive analysis of the methods for diagnostics and monitoring of MPS is presented. Laboratory (urine analysis for GAG, enzyme analysis, molecular genetic analysis) and instrumental methods (X-ray, CT, ultrasound, MRI) are described, with an emphasis on MRI as the "gold standard" for CNS visualization. Universal programs for processing MRI images (SPM, FreeSurfer, 3D Slicer, BrainSuite) and image correction methods (Gaussian, median, Fourier, anisotropic diffusion, wavelet filtering), their advantages and limitations in MPS are considered. The problems of assessing disease progression are discussed, including variability of the course, limited availability of quantitative analysis methods and the inaccuracy of existing segmentation algorithms in the presence of pronounced structural changes. The role of AI and ML in solving these problems is emphasized. The article covers the use of biomarkers and the need to standardize diagnostic approaches. The importance of interdisciplinary interaction (genetics, neurology, radiology, bioinformatics) is substantiated. Practical significance. The results of the analysis systematize information on modern diagnostic and monitoring capabilities for MPS. A review of advanced technologies, such as AI and image processing methods, informs specialists about the potential for their implementation to improve the accuracy of diagnosis and assess the dynamics of the disease. The identified problems and proposed directions (standardization, development of new algorithms, integration of biomarkers, interdisciplinary approach) can serve as a basis for further research and improvement of clinical practice in managing patients with MPS, contributing to improving their quality of life and the effectiveness of treatment.