Integrated index for cardiac arrythmias diagnosis using entropies as features of heart rate variability signal

An electrocardiogram (ECG) represents the electrical activity of the heart and can be used to investigate cardiac health. Routinely subtle changes in the ECG's P-QRS-T wave are employed to depict a particular type of cardiac abnormality. Herein, we have used heart rate (derived from ECG) as the base signal for our analysis, to demonstrate how effectively we can differentiate cardiac rhythm abnormalities. The normal heart rate varies continuously between 60 to 100 bpm. These signals are highly nonlinear and non-stationary in nature. In this work, we have analyzed 352 subjects from nine different cardiac classes. Hence, we have used nonlinear entropies as features of these heart rate signals. We have then proposed a novel Integrated Index combining these entropies, to diagnose cardiac rhythm abnormalities. This one Index value can now be used to differentiate different cardiac classes.