Vol 52, No 2 (2018)

Specific design features and main characteristics of a compact accelerator-based epithermal neutron source for boron neutron capture therapy are described. High quality of the neutron flux generated by the accelerator-based source has been experimentally confirmed. The opportunities for medical use of the accelerator-based epithermal neutron source in oncological centers for boron neutron capture therapy are assessed.

In this work, the possibility of controlling radiation power of a surgical carbon dioxide laser using an electromagnetic control system is considered. It is demonstrated that the electromagnetic control system makes it possible to adjust the laser radiation power within a wide range, allowing thereby the same laser system to be used for various medical purposes. Experimental results on the impact of modulated laser radiation on biological tissues are presented.

A device for passive mechanotherapy of lower limbs was developed to meet the demand for automated systems for mechanotherapy. The device provides accelerated rehabilitation after injuries of the musculoskeletal system and strokes.

A method using modeling on a human head phantom to evaluate the total targeting error of a stereotactic instrument to deep targets in patient’s brain is presented. The means of targeting the instrument to intracerebral aiming points was a Medtronic StealthStation S7 frameless navigation system combined with a Philips Achieva 3 T MRI scanner and a Philips Gemini TF PET/CT scanner.

We review additive techniques used for manufacturing orthopedic prosthetic devices. Studies using 3D scanners and built-in software are assessed. 3D scanners are analyzed after developing a scanning method and a method of assessing convenience and scan quality. Software for modeling orthopedic prosthetic devices is discussed.

Modern mathematical methods for analysis of heart rate variability (HRV), such as rescaled range analysis, detrended fluctuation analysis, and phase-rectified signal averaging were considered. Algorithms for calculating novel nonlinear HRV indices were described in detail. Mathematical models for simulating artificial cardiac Beat-to-beat intervals that take into account various noise processes were created. A state model of the cardiovascular system based on HRV analysis was developed. The sensitivity of HRV indices to changes in the state of the cardiovascular system was theoretically estimated using artificially simulated data.

To solve important problems of cardiovascular monitoring, effective algorithms for computer processing of electrocardiogram signals (ECS) should be developed on the basis of nonlinear dynamic analysis. ECS can be represented as electric excitation of the conducting nerve network of the heart (CNNH) in the form of solitons of different sizes, taking into account their polarization along the main CNNH branches. Detailed information on the electrical activity in all parts of the four-chamber heart is contained in the self-similar fractal scale-invariant CNNH structure. With the help of wavelet transform, it is possible to represent the structure of the process of excitation of CNNH segments as a system of local extrema of the wavelet diagram of ECS. The wavelet spectrum of ECS has a fractal structure in the form of self-similar waves with scaling 1/f. Each of these waves reflects the excitation of the corresponding CNNH segment. Wavelet representation of the ECS can be used as a tool for detecting various cardiovascular diseases by visualizing skeleton functions of the ECS wavelet transform.

A natural relationship between the probabilistic and fuzzy set approaches to the intelligent analysis of the level of risk of drug dependence was demonstrated using results of psychological testing of students. A method of forming a Bayesian model of linguistic variables characterizing the initial signs of the predisposition to drugs is presented. Steps of fuzzy logical inference of the aggregate informative classifier for ranking and classifying subjects’ psychoemotional status are considered within the framework of hierarchy analysis and principal components analysis.

In this article we propose an algorithm for the analysis of multiple relationships between electroencephalogram (EEG) channels. This algorithm identifies groups of linked channels and estimates the strength of relationships within these groups. This is a novel approach that can detect informative cortical zones for particular psychoneurological diseases. It can also be used to create new systems for processing EEG data and diagnose diseases.