Application of artificial metaplasticity neural networks to cardiac arrhythmias classification

Abstract

Correct diagnosis of cardiac arrhythmias is one of the major problems in medical field. Cardiac arrhythmias can be early detected and diagnosed to prevent the occurrence of heart attack as well as the consequent deaths. An effective method for early detection of these arrhythmias, and thus to procure early treatment, is necessary. In this research we have applied artificial metaplasticity multilayer perceptron (AMMLP) to cardiac arrhythmias classification. The MIT-BIH Arrhythmia Database was used to train and test AMMLPs. The obtained AMMLP classification accuracy of 98.25%, is an excellent result compared to the classical MLP and recent classification techniques applied to the same database. © 2013 Springer-Verlag.