Arrhythmias classification using the fractal behavior of the power spectrum density of the QRS complex and ANN

In this paper we propose a method to discriminate arrhythmias using the fractal behavior of the power spectrum density of the QRS complexes. The linear interpolation of the QRS complex power spectrum density in Bode diagram in two different frequency intervals gives two straight lines with two different slopes. The scatter plot of one slope versus the other shows that we can distinct the normal beats from the abnormal one. Therefore, two experiences have been elaborated to verify the usefulness of the proposed method. The PVC beats are clustered using a Self Organizing Map (SOM) neural network fed by the two slopes of the QRS complex power spectrum in the first experience, in the second experience we use multilayer perceptron (MLP) neural network to classifier right bundle branch block (RBBB) and normal beats. The MIT/BIH arrhythmia database is then used to evaluate the usefulness of the proposed method.