Methodological Approach to Automated Recognition of Atrial Fibrillation and Subsequent Classification

This study considers topical issues aimed at improving the methodology of early recognition of atrial fibrillation and monitoring its treatment against the background of other heart rhythm disorders. The task set in this study is an essential component of the search for solutions, whose purpose is to increase the efficiency of information systems for cardiac diagnostics and monitoring within the framework of complex research to improve the means of heart rhythm analysis and arrhythmia recognition. In the context of this study, a linear discriminant analysis approach based on the concept of K-entropy was proposed as a means of automating the procedure for the recognition of AF against the background of other rhythm disorders using a limited data sample. With regard to the classification of atrial fibrillation samples, the use of decisive rules and arrhythmia types, based on the analysis of scatterograms, is put forth as a solution. The results of the proposed methods for recognizing the presence of atrial fibrillation and its classification demonstrated superior performance when compared to existing methods. The proposed method exhibited a specificity of 98.5% and a sensitivity of 98%. The proposed method for determining the presence of atrial fibrillation demonstrates suboptimal accuracy when applied to a limited sample size. Further development of the method should be concentrated in this area.