Experience of Studying Neural Network Modeling in the Professional Education Program Using the Example of Detecting R-peaks of a Cardiogram
Abstract
In Russia, the Digital Economy program is currently being implemented, which is aimed at developing digital technologies and infrastructure. Within the framework of this program, specialists for the IT sphere and related industries with digital competencies are trained. The importance of digital competencies for a modern specialist lies in increasing his efficiency and productivity, the ability to adapt to changing conditions, and the ability to find new ways to solve problems. Moscow State University of Psychology and Education (MSUPE) offers students the professional education program "Software Development Tools for Solving Problems in Psychology and Education", which includes mastering Python programming and solving applied problems of analyzing psychological research data with it, including using machine learning and neural networks. The article describes one of the case assignments completed by students and including processing an electrocardiogram signal in order to detect R-peaks in it. Completing the assignment includes collection of ECG data with BiTronics LAB and building a data analysis model using Python. The description of the used neural network model based on LSTM recurrent layers is given, and the reliable operation of this model in processing ECG signals is demonstrated.
General Information
Keywords: neural networks, LSTM, data analysis, electrophysiology, electrocardiogram, BiTronics, R-peaks
Publication rubric: Intelligent Technologies in Humanities and Education
Article type: theses
For citation: Alekseychuk A.S. Experience of Studying Neural Network Modeling in the Professional Education Program Using the Example of Detecting R-peaks of a Cardiogram. Digital Humanities and Technology in Education (DHTE 2024): Collection of Articles of the V International Scientific and Practical Conference. November 14-15, 2024 / V.V. Rubtsov, M.G. Sorokova, N.P. Radchikova (Eds). Moscow: Publishing house MSUPE, 2024., pp. 340–350.
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