The aim of the study was to investigate whether visual stimuli have the same potency to increase electroencephalography (EEG) delta wave power density during non-rapid eye movement (NREM) sleep as do auditory stimuli that may be practical in the treatment of some sleep disturbances. Nine healthy subjects underwent two polysomnography sessions—adaptation and experimental—with EEG electrodes positioned at Fz–Cz. Individually adjusted auditory (pink noise) and visual (light-emitting diode (LED) red light) paired 50-ms signals were automatically presented via headphones/eye mask during NREM sleep, shortly (0.75–0.90 s) after the EEG wave descended below a preset amplitude threshold (closed-loop in-phase stimulation). The alternately repeated 30-s epochs with stimuli of a given modality (light, sound, or light and sound simultaneously) were preceded and followed by 30-s epochs without stimulation. The number of artifact-free 1.5-min cycles taken in the analysis was such that the cycles with stimuli of different modalities were matched by number of stimuli presented. Acoustic stimuli caused an increase (p < 0.01) of EEG power density in the frequency band 0.5–3.0 Hz (slow waves); the values reverted to baseline at post-stimuli epochs. Light stimuli did not influence EEG slow wave power density (p > 0.01) and did not add to the acoustic stimuli effects. Thus, dim red light presented in a closed-loop in-phase fashion did not influence EEG power density during nocturnal sleep.

To solve the problems associated with large data volume real-time processing, heterogeneous systems using various computing devices are increasingly used. The characteristic of solving this class of problems is related to the fact that there are two directions for improving methods of real-time data analysis: the first is the development of algorithms and approaches to analysis, and the second is the development of hardware and software. This article reviews the main approaches to the architecture of a hardware-software solution for traffic capture and deep packet inspection (DPI) in data transmission networks with a bandwidth of 80 Gbit/s and higher. At the moment there are software and hardware tools that allow designing the architecture of capture system and deep packet inspection: 1) Using only the central processing unit (CPU); 2) Using only the graphics processing unit (GPU); 3) Using the central processing unit and graphics processing unit simultaneously (CPU + GPU). In this paper, we consider these key approaches. Also attention is paid to both hardware and software requirements for the architecture of solutions. Pain points and remedies are described.

Nowadays, analog electrocardiographs that deliver only paper printout are ubiquitous in medical institutions. Doctors do visual analysis of electrocardiograms (ECG), occasionally using measurement tools. This article reviews approaches to automatic analysis of electrocardiogram images, including the signal conversion from paper to digital format. The following methods are presented: digitizing graphs from images, determination of signal nodes, and preparation of final report. Various methods of computer vision were tested on electrocardiogram images in order to highlight the graph and transfer coordinates to millimeters. Their limitations are identified and described. Based on the evaluation, a suitable electrocardiogram analysis method has been developed. It includes color filtering of the background grid. Methods of signal analysis and reading of indicators, and their further analysis, are also given. The text conclusion is based on decision trees traversal. As a result, the architecture of measuring system software for electrocardiogram analysis was developed. The system is described considering that the electrocardiogram evaluation unit does not depend on external implementation and can be reused in other systems performing electrocardiogram analysis.

This article describes the developed architecture of the system module for processing and interpreting analog medical data. Patients often undergo examinations in various medical institutions, and since their results are often handed out to the patient in printed form, the receiving institution transfers them to its database manually. There is also a tendency to completely refuse analog media and use only digital ones. But in this case, another problem appears - either loss or conversion of the accumulated analog base into digital format. These days, automatic document management systems for medical institutions - Health information systems (HIS) - are actively developing. The software module developed in accordance with the architecture described in the article can be used by developers of various HIS to automate the work with analog data. If it is necessary, it can also be freely expanded by adding new modules for working with various analog data. In this article, we take ECG scans and medical test results as examples of such data. As a result of the work undertaken the prototype of the designed system was developed and tested.

This paper describes a method for collecting information of user's emotional state based on its facial expressions and linking the data acquired to the user's actions in a time. Methods and Algorithms: At client side, we use reactive programming model implemented by RxJS library to manage data streams. Our client library includes logic to combining events from different sources such as MediaStream APIs, classic DOM event API. We have developed our own neural network architecture that allows us to map the facial expressions of people into a continuous space of arousal/valence. The same architecture for classification of emotions was used. We used inception and residual blocks and batch normalization layers to achieve higher accuracy. In addition, we augment train data by adding pose variations using 3D face reconstruction. Results: We have developed a system that allows us to collect various data such as video stream and events of user interaction with the webpage. We also trained a neural network that shows high accuracy. One part of the system is a client library that does not require the user to install any special software and can be easily integrated into the website. The other part is a server application that enables the site owner to analyze the data collected.

Brainwave entrainment is used in different types of treatment, one of which is treating affective and psychosomatic disorders. The better result is achieved if individual alpha-peak frequency is taken into account during entrainment and when it includes audio visual and tactile stimulation. The program which allows running stimulation scenarios is required to be available on different platforms because patients need to continue the procedures even after finishing them in hospital. So they need to be able to download and install this control program on their personal computers. The required application was developed as a multiplatform one using. NET Core framework in order to reduce costs of further maintenance.