A probability density function method for detecting atrial fibrillation using R–R intervals

A probability density function (PDF) method is proposed for investigating the structure of the reconstructed attractor of R–R intervals. By constructing the PDF of distance between two points in the reconstructed phase space of R–R intervals of normal sinus rhythm (NSR) and atrial fibrillation (AF), it is found that the distributions of PDF of NSR and AF R–R intervals have significant differences. By taking advantage of their differences, a characteristic parameter kn, which represents the sum of n points slope in filtered PDF curve, is put forward to detect both 400 segments of NSR and AF R–R intervals from the MIT-BIH Atrial Fibrillation database. Parameters such as number of R–R intervals, number of embedding dimensions and slope are optimized for the best detection performance. Results demonstrate that the new algorithm has a fast response speed with R–R intervals as short as 40, and shows a sensitivity of 0.978, and a specificity of 0.990 in the best detecting performance.