Processing the results of electroencephalography for patients suffering from depression after neuro-electrostimulation course: Case study

The article presented the results of electroencephalography (EEG) signal processing in a case study of neuro-electrostimulation application for patients suffering from depression. Neuro-electrostimulation was performed by the SYMPATHOCOR-01 device in two modes - multichannel and single-channel stimulation. The analysis of changes in the EEG activity maps during neuro-electrostimulation course was carried out. The common conclusion for all patients is an increase in the homogeneity for the distribution of spectral power density for EEG signals. A quantitative method for estimating the level of the brain zones activation was proposed. For patients from the multichannel stimulation group, an increase in the activation level was observed. It was noted that for patients from the single-channel stimulation group there were zones in which a significant decrease in the level of activation was observed. © 2019 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.Russian Foundation for Basic Research, RFBR: 18-29-02052Government Council on Grants, Russian FederationThe EEG signals data acquisition within the study (Chapter 2) was supported by the Act 211 of the Government of the Russian Federation (contract no. 02.A03.21.0006). The EEG signals data processing (Chapter 3) was funded by RFBR (project no, 18-29-02052)

Mobile Hardware-Information System for Neuro-Electrostimulation

The article describes organizational principles of the mobile hardware-informational system based on the multifactorial neuro-electrostimulation device. The system is implemented with two blocks: the first block forms the spatially distributed field of low-frequency monopolar current pulses between two multielement electrodes in the neck region. Functions of the second block, specialized control interface, are performed by a smartphone. Information is exchanged between two blocks through a telemetric channel. The mobile hardware-informational system allows to remotely change the structure of the current pulses field, to control its biotropic characteristics and to change the targets of the stimulation. Moreover, it provides patient data collection and processing, as well as access to the specialized databases. The basic circuit solutions for the neuro-electrostimulation device, implemented by means of microcontroller and elements of high-level hardware integration, are described. The prospects of artificial intelligence and machine learning application for treatment process management are discussed. © 2018 Vladimir S. Kublanov et al.)is study was supported by the Act 211 of the Government of the Russian Federation (contract no. 02.A03.21.0006) and was funded by RFBR (project no, 18-29-02052)

Towards a decision support system for disorders of the cardiovascular system diagnosing and evaluation of the treatment efficiency

The study describes a preliminary stage of the decision support system development for cardiovascular system disorders. As the clinical model of the disorders, the arterial hypertension was used. The study consisted of two steps: diagnosing of the arterial hypertension and an evaluation of the treatment efficiency during the neuro-electrostimulation application. For the diagnosing part, a clinical study was conducted involving heart rate variability signals recording while performing tilt-test functional load. Performance of different machine learning techniques and feature selection strategies in task of binary classification (healthy volunteers and patients suffering from arterial hypertension) were compared. The genetic programming feature selection and quadratic discriminant analysis classifier reached the highest classification accuracy. Best feature combinations were used to evaluate a treatment efficiency. The results indicate the potential of the proposed decision support system. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserve

Using the dynamic mapping of the microwave brain radiation for functional studies

In this study, organizational principles of the radiophysical complex are proposed for simultaneous measurements of the brightness temperature fluctuations of the human brain and parameters of the autonomic nervous system. A new structure of the medical microwave radiothermograph is suggested based on the parametric compensation of losses in the circuit elements of the thermobalance. The aim of this article is to justify the structural organization of the radiophysical complex for the recording and joint real-time analysis of processes of the microwave radiation formation in the brain tissue and characteristics of the autonomic nervous system. Description of the radiophysical complex MRTHR with realization of these principles is presented. The peculiarity of the proposed radiophysical complex is in passive elements that do not contain electronic nodes and blocks and are inside the shielded cabin. In order to increase the accuracy of the brightness temperature of the deep brain structures measurement in the radiophysical complex a new structure is implemented. This structure of the medical microwave radiothermograph is based on the parametric compensation of losses in the circuit elements of the thermobalance

Optimal Combination of Mother Wavelet and AI Model for Precise Classification of Pediatric Electroretinogram Signals

The continuous advancements in healthcare technology have empowered the discovery, diagnosis, and prediction of diseases, revolutionizing the field. Artificial intelligence (AI) is expected to play a pivotal role in achieving the goals of precision medicine, particularly in disease prevention, detection, and personalized treatment. This study aims to determine the optimal combination of the mother wavelet and AI model for the analysis of pediatric electroretinogram (ERG) signals. The dataset, consisting of signals and corresponding diagnoses, undergoes Continuous Wavelet Transform (CWT) using commonly used wavelets to obtain a time-frequency representation. Wavelet images were used for the training of five widely used deep learning models: VGG-11, ResNet-50, DensNet-121, ResNext-50, and Vision Transformer, to evaluate their accuracy in classifying healthy and unhealthy patients. The findings demonstrate that the combination of Ricker Wavelet and Vision Transformer consistently yields the highest median accuracy values for ERG analysis, as evidenced by the upper and lower quartile values. The median balanced accuracy of the obtained combination of the three considered types of ERG signals in the article are 0.83, 0.85, and 0.88. However, other wavelet types also achieved high accuracy levels, indicating the importance of carefully selecting the mother wavelet for accurate classification. The study provides valuable insights into the effectiveness of different combinations of wavelets and models in classifying ERG wavelet scalograms. © 2023 by the authors.Ministry of Education and Science of the Russian Federation, MinobrnaukaThe research funding from the Ministry of Science and Higher Education of the Russian Federation (Ural Federal University Program of Development within the Priority—2030 Program) is gratefully acknowledged

Search for Significant Parameters of Heart Rate Variability for Assessing Athletes' Fitness

The work conducted a search for the relationship between the parameters of heart rate variability and athletic performance. It is shown that among 64 parameters for different groups of athletes, one can distinguish those parameters that statistically significantly change during the training process. The parameters are different for a different groups of athletes. © 2020 American Institute of Physics Inc.. All rights reserved.The authors want to express gratitude to the School of the Olympic Reserve, especially Zotova Natalia Vladimirovna for assistance in organizing the study. The work was supported by Act 211 Government of the Russian Federation, contract N 02. A03.21.0006