Cluster-analysis of human pulse wave by self-organizing Cohonen maps to identify markers of age groups

Abstract

The present study is methodological in nature and describes an approach to the study of the human cardiovascular system. The assessment of biological age is important for the management of the human aging process. Regular monitoring of this indicator makes it possible to take preventive measures to reduce the likelihood of cardiovascular disasters. The aim of the study is to determine the age groups based on the pulse waves of the subjects using Kohonen neural networks. In this study, Kohonen self-organizing maps were used to cluster pulse waves of people of different ages by observing the dy-namic change of the pulse wave with the selection of parameters of the neural network self-organization algorithm. A data set of different age groups was used. Each group con-tained files of subjects of the same age, each file – pulse waves of these subjects. As a result of experiments with neural network configurations, cluster structures were obtained and geometric characteristics of pulse waves corresponding to age groups were estab-lished. The clustering algorithm used broke the pulse waves into clusters, the reliability of which in some cases reached 91–96 %. The software implementation of the trained neural network and the portability of the pulse wave measuring device allow the proposed tech-nique to be used to monitor the state of the cardiovascular system in people at risk of cardiovascular disasters, as well as people associated with dangerous professions, ath-letes, office workers, etc. Registration of changes in biological age contributes to the adoption of timely preventive measures, decisions on the modification of nutrition, life-style and physical activity. The resulting network can be used in diagnostic systems to support medical decision-making.