Keynote speakers
Steshenko Vladimir Borisovich
Trends in the Development of EEE parts of DSP equipment for space aplications
Abstract: The electrical, electronic and electromechanical parts used in the on-board equipment of the spacecraft must fully ensure the compliance with the target engineering specifications of the electronic equipment in terms of functional and electrical parameters, as well as resistance to external influencing factors. The report analyzes the history of development and the current state of the electronic components used in on-board signal processing systems. A medium-term roadmap for the development of the EEE parts is presented. disorders and advancing medical interventions
Dr. Koushlendra Kumar Singh
Unveiling the Mysteries of the Human Brain: Exploring EEG Signals, Applications, and Seizure Prediction
Abstract: This lecture delves into the complexities of the human brain, emphasizing its role as the central command center governing various bodily functions. The focus is on electroencephalography (EEG) signals, the electrical manifestations of brain activity. From understanding the basics of EEG signals to exploring their applications, monitoring devices, and electrode placements, the presentation navigates through the landscape of EEG technology. A pivotal segment addresses the challenges posed by brain disorders, laying the foundation for the exploration of EEG data collection processes. The discussion extends to diverse EEG tests, signal processing techniques, and their applications in addressing sleeping disorders. Notably, the presentation addresses a compelling problem statement – the impact of epilepsy on individuals and society at large. The latter part unfolds a detailed case study on epilepsy, delving into estimation devices and proposing a solution: the design and development of a seizure prediction method. The objectives encompass the development of effective seizure prediction algorithms, automation capabilities for triggering seizure-neutralizing procedures, and the utilization of deep learning models for classifying ictal and pre-ictal phases. The experiment analysis provides insights into the dataset, data pre-processing steps, and the methodologies applied for channel selection. The presentation concludes with an overview of the work, showcasing experimental results, findings, and the potential implications of this research in enhancing our understanding of brain disorders and advancing medical interventions
Khryashchev Vladimir Vyacheslavovich
Artificial intelligence for medical decision support system in endoscopy: tasks, problems, trends
Abstract: Digital image processing and artificial intelligence methods are now used to diagnose many diseases. They are the basis for building medical decision support systems. For example, one of the tasks is the use of modern algorithms to create diagnostic automation systems for endoscopic examinations of the gastrointestinal tract. The high variability of images of the mucous membranes and numerous objects of interest in endoscopic images determine high complexity of such procedures. This makes it relevant to use systems for automatic detection and classification of objects of interest in endoscopy. At the same time, the accuracy of diagnostics increases, the influence of human factor on the quality of research is significantly reduced, and the cost and time spent on conducting them is also decreasing. Such systems can be used both to train new specialists and to improve the skills of experienced medical personnel. The report summarizes the practice of building and testing the developed system for supporting medical decision-making during endoscopy of the gastrointestinal tract in three Russian medical centers.