Heart rate variability analyzed by computer science

Abstract

Computer science is being used in medical applications with very good results. In this work, artificial neural networks are used as intelligent techniques that allow analyzing the heart rate variable to anticipate changes in the autonomic nervous system, which seeks to maintain the homeostasis of the organism and the execution of adaptive responses to environmental changes. This clinical examination has been little explored, and we want to contribute with this work to correct ectopic beats and artifacts detected in an electrocardiographic signal for the precise calculation of heart rate variability. The reverse propagation algorithm is used. The ectopic beat detection and correction program is validated based on the calculation of the sensitivity, specificity, and accuracy of ectopias and artifacts detection. A sample of 40 electrocardiogram signals is taken using for training and testing. With the proposed method, an accuracy of 82.34%, a sensitivity of 89.22% and a specificity of 95.78%.