Arrhythmia disease classification using Artificial Neural Network model

In this paper we proposed an automated Artificial Neural Network (ANN) based classification system for cardiac arrhythmia disease using standard 12 lead ECG signal recordings. In this study, we are mainly interested in classifying different arrhythmia types (classes) using multilayer peceptron (MLP) model. We have used UCI ECG signal data to train and test MLP network model. For this multi class classification we used one arrhythmia class against normal arrhythmia class. Different arrhythmia types include coronary artery disease, old anterior myocardial infarction, old inferior myocardial infarction, sinus tachycardia, sinus bradycardia, right bundle branch block etc. In arrhythmia analysis, it is unavoidable that some attribute values of a person would be missing. Therefore we have replaced these missing attributes by closest column value of the concern class. MLP feedforward neural network model is trained by static backpropagation algorithm with momentum learning rule to classify cardiac arrhythmia classes. The classification performance is evaluated using measures such as classification accuracy, training, testing and cross validation mean squared error (MSE), percentage correct, receiver operating characteristics (ROC) and area under curve (AUC). From careful and exhaustive experimentation, we reached to the conclusion that proposed classifier gives best classification results in terms of classification accuracy of 100 % for classes 1 and 2, 98.72%, 97.4%, 94.25%, 92.1% for classes 4, 5, 2 and 10 respectively.