biogeography-based optimization of neuro-fuzzy system parameters for diagnosis of cardiac disease
Cardiomyopathy refers to diseases of the heart muscle that becomes enlarged, thick, or rigid. These changes affect the electrical stability of the myocardial cells, which in turn predisposes the heart to failure or arrhythmias. Cardiomyopathy in its two common forms, dilated and hypertrophic, implies enlargement of the atria; therefore, we investigate its diagnosis through P wave features. In particular, we design a neuro-fuzzy network trained with a new evolutionary algorithm called biogeography-based optimization (BBO). The neuro-fuzzy network recognizes and classifies P wave features for the diagnosis of cardiomyopathy. In addition, we incorporate opposition-based learning in the BBO algorithm for improved training. First we develop a neuro-fuzzy model structure to diagnose cardiomyopathy using P wave features. Next we train the network using BBO and a clinical database of ECG signals. Preliminary results indicate that cardiomyopathy can be reliably diagnosed with these techniques.
biogeography-based optimization neuro-fuzzy system evolutionary algorithm electrocardiogram