Data fusion for heart diseases classification using multi-layer feed forward neural network

In this paper, classification of the heart diseases using the heart rate variability signals was performed in order to discriminate between normal subjects and patients with low heart rate variability such as patients suffering from congestive heart failure (CHF) and myocardial infarction diseases. A multi-layer feed forward neural network was utilized. For each of the three groups under investigation, three different techniques were used to select the inputs to the proposed classifier. These techniques are time-domain methods, frequency-domain methods, and non-linear methods. Results have shown that using non-linear methods give high rates for classifying heart diseases. Classification rate reaches to 96.36%. In an attempt to improve the classification rate, data fusion at feature extraction level was adopted. A new feed forward neural network was designed. It gives an average classification rate of 98.18%.