Yavorskyi A. Methods and Algorithms for Human Biosignals Analysis Using Machine Learning

The aim of the work is to improve the quality of electrocardiogram (ECG) analysis by developing methods and algorithms for more accurate determination of deviations in pre-processed human biosignals in order to minimize health threats and to impact the quality-of-life improvement in a timely manner. The basis of the developed methods are approaches based on machine learning, which were tested on various available large datasets, which proved the high efficiency of the proposed methods. Human life and health are the advantage of constitutions of many states, and in general, the task for many industries and research. However, according to the statistics of the World Health Organization, more than a quarter of deaths are caused by cardiovascular diseases. At the same time, early diagnosis and intervention of professionals could significantly improve the situation. Therefore, more accurate analysis and continuous monitoring in groups at risk would help to improve the human’s quality of life, especially those at risk for cardiovascular diseases. Thus, more mobile devices in use, which allowed for real-time ECG monitoring, and development of more accurate diagnostic methods, on the other hand, would allow to achieve the goal and reduce the number of deaths.